Jayashan-cb004082-Criminal Face Recognition-Final

Anton Jayashan cb004082

APIIT Sri Lanka of 151

Project Approval

Approval of the Project Manager

…………………………….

Mr.Javed Ahsan

(Project Manager)

I certify that this thesis satisfies the requirements as a thesis for the degree of

Bachelor of Science in Engineering (Hons).

……………………………………

Dr.Dhananjaya Kulkarni

(Supervisor)

I certify that I have read this thesis and that in my opinion it is fully adequate in

scope and quality as a thesis for the Bachelor of Science in Engineering (Hons).

……………………………………

Mrs. Chathura Sooriyaarachchi

(Assessor)



Abstract

Criminal record generally contains personal information about particular person along with

photograph. To identify any Criminal we need some identification regarding person, which

are given by eyewitness. In most cases the quality and resolution of the recorded image

segments is poor and hard to identify a face. To overcome this sort of problem we are

developing software. Identification can be done in many ways like finger print, eyes, DNA

etc. One of the applications is face identification. The face is our primary focus of attention in

social inters course playing a major role in conveying identify and emotion. Although the

ability to infer intelligence or character from facial appearance is suspect, the human ability

to recognize face is remarkable.



Acknowledgment

First of all, let me thank the Almighty God and my parents who are the most graceful and

merciful for their blessings that contributed to the successful completion of my project work.

I am greatly thankful to my supervisor, Dr.Dhananjaya Kulkarni for his immense guidance,

support and supervision to make this effort a success. Without his invaluable supervision, all

my efforts could have been short-sighted.

I am also grateful to Mrs. Chathura Sooriyaarachchi as being my student assessor and guiding

me with the proper instructions and knowledge.

I would like to thank the Asia Pacific Institute of Information Technology for providing me the

facilities of computer laboratories, library and studying environment.

I would like to thank all my friends, relatives, and tech room and library assistants for their

support.

Author owes quite a lot to my family who provided me the chance to fulfil my career objectives

and for the support throughout the studies. Author would like to dedicate this study to them as

an indication of their significance in this study, as well as in my life. It has been a great

experience to my life.

Finally, I am responsible for all the errors remain in this documentation.



Table of Contents

Abstract ...................................................................................................................................... 2

Acknowledgment ....................................................................................................................... 3

LIST OF FIGURES ................................................................................................................... 9

Chapter 1 .................................................................................................................................. 13

1 Introduction ...................................................................................................................... 13

1.1 Project background .................................................................................................... 13

1.2 Understanding the problem ....................................................................................... 14

1.3 Project objectives ...................................................................................................... 15

1.4 Proposed solution ...................................................................................................... 16

1.5 Project scope ............................................................................................................. 17

1.5.1 Accurate identification of faces ......................................................................... 17

Chapter 2 .................................................................................................................................. 18

2 Domain investigation ....................................................................................................... 18

2.1 Over View face recognition system .......................................................................... 18

2.2 Similar systems ......................................................................................................... 19

2.2.1 FaceIT ................................................................................................................ 19

2.2.2 ZN-Face ............................................................................................................. 20

2.2.3 Google picasa ..................................................................................................... 22

2.2.4 APPLE IPhoto .................................................................................................... 23

2.3 Problem of the face recognition systems................................................................... 24

2.3.1 Pose variance ..................................................................................................... 24

2.3.2 Lighting/illumination variance........................................................................... 24

2.3.3 Different facial occlusions ................................................................................. 25

2.3.4 Makeup challenges............................................................................................. 25

Chapter 3 .................................................................................................................................. 27

3 System research and investigation ................................................................................... 27

3.1 Fundamental of image processing ............................................................................. 27

3.2 Image classification ................................................................................................... 27

3.3 Image segmentation................................................................................................... 28

Segmentation approaches................................................................................................. 28

3.3.1 Threshold based segmentation ........................................................................... 29

3.3.2 Edge based segmentation ................................................................................... 30

3.3.3 Edge detection .................................................................................................... 31

3.4 Face detection approaches ......................................................................................... 36

3.4.1 Controlled environment ..................................................................................... 37

3.4.2 Colour images .................................................................................................... 37



3.4.3 Images in motion................................................................................................ 37

3.4.4 Face detection difficulties .................................................................................. 38

3.5 Face detection methods ............................................................................................. 39

3.5.1 The Viola-Jones Face Detector .......................................................................... 40

3.5.2 Local Binary Patterns (LBP) .............................................................................. 45

3.5.3 AdaBoost Algorithm for face detection ............................................................. 47

3.5.4 Neural Network based face detection ................................................................ 49

3.6 Face Region extraction approaches ........................................................................... 51

3.6.1 Template matching............................................................................................. 51

3.6.2 Haar like features-Viola Jones ........................................................................... 52

3.7 Face recognition Methods ......................................................................................... 55

3.7.1 Holistic matching methods ................................................................................ 56

3.7.2 Eigenfaces .......................................................................................................... 56

3.7.3 Principal component analysis ............................................................................ 58

3.7.4 Fisherfaces ......................................................................................................... 60

3.7.5 Template matching face recognition methods ................................................... 61

CHAPTER 4 ............................................................................................................................ 62

4 REQUIRMENTS SPECIFICATION .............................................................................. 62

4.1 System requirements ................................................................................................. 62

4.1.1 Functional requirements..................................................................................... 62

4.1.2 Non-functional requirements ............................................................................. 66

4.2 Resource requirements .............................................................................................. 66

4.2.1 Hardware requirements ...................................................................................... 67

4.2.2 Software requirements ....................................................................................... 68

CHAPTER 5 ............................................................................................................................ 69

5 DEVELOPMENT METHODOLOGY ............................................................................ 69

5.1 Spiral model .............................................................................................................. 70

5.1.1 Diagram of spiral ............................................................................................... 71

5.1.2 Advantages of spiral .......................................................................................... 71

5.1.3 Disadvantages of spiral ...................................................................................... 71

5.1.4 When to use spiral .............................................................................................. 72

5.2 Waterfall Methodology ............................................................................................. 72

5.2.1 Advantages of Waterfall .................................................................................... 73

5.2.2 Disadvantages of Waterfall ................................................................................ 73

5.3 Agile methodology .................................................................................................... 74

5.3.1 Advantages of Agile .......................................................................................... 75

5.3.2 Disadvantages of Agile ...................................................................................... 75



5.4 Hybrid model............................................................................................................. 76

5.4.1 Why we choose Hybrid Model .......................................................................... 76

5.5 Justification of selection method ............................................................................... 78

CHAPTER 6 ............................................................................................................................ 79

6 TECHNICAL RESEARCH AND INVESTIGATION ................................................... 79

6.1 Developing platforms ................................................................................................ 79

6.1.1 Dot.net ................................................................................................................ 79

6.1.2 .Net Languages .................................................................................................. 81

6.2 Sun Java..................................................................................................................... 82

6.3 Java architecture ........................................................................................................ 82

6.4 Compilation of Code ................................................................................................. 83

6.4.1 Characteristics of Java ....................................................................................... 83

6.5 Image processing APIs .............................................................................................. 84

6.5.1 OpenCv .............................................................................................................. 84

6.5.2 EmguCV ............................................................................................................ 84

6.5.3 Aforge.Net ......................................................................................................... 85

6.6 Developing approaches ............................................................................................. 85

6.6.1 Approach one ..................................................................................................... 85

6.6.2 Approach two ..................................................................................................... 86

6.6.3 Approach three ................................................................................................... 86

6.7 Justification of selection of programming environment ........................................... 86

CHAPTER 7 ............................................................................................................................ 88

7 SYSTEM DESIGN .......................................................................................................... 88

7.1 Use case diagrams ..................................................................................................... 89

7.1.1 Use case diagram for admin ............................................................................... 91

7.1.2 Use case diagram for Admin and Criminal........................................................ 92

7.2 Use case descriptions ................................................................................................ 93

7.2.1 Add new record .................................................................................................. 93

7.2.2 View record ........................................................................................................ 94

7.2.3 Train image space .............................................................................................. 95

7.2.4 Find the matched image ..................................................................................... 96

7.3 System Overview ...................................................................................................... 97

7.4 E-R Diagrams (Process flow) .................................................................................... 98

7.4.1 E-R diagram between administrator and the operator ....................................... 98

7.4.2 E-R diagram between operator and the criminal ............................................... 98

7.5 Activity Diagrams ..................................................................................................... 99

7.5.1 Administrator Process ...................................................................................... 100



7.5.2 Operator Process .............................................................................................. 103

7.6 Sequence diagram ................................................................................................... 104

7.6.1 Administrator ................................................................................................... 104

7.6.2 System Operator............................................................................................... 105

7.7 Collaboration diagrams ........................................................................................... 106

7.7.1 Administrator ................................................................................................... 106

7.7.2 Operator ........................................................................................................... 107

7.8 Component Diagram ............................................................................................... 108

7.9 Deployment Diagram .............................................................................................. 109

7.10 Class Diagram...................................................................................................... 110

CHAPTER 8 .......................................................................................................................... 111

8 TESTING AND EVALUATION .................................................................................. 111

8.1 Test scope ................................................................................................................ 111

8.1.1 Unit testing ....................................................................................................... 112

8.1.2 Integration testing ............................................................................................ 112

8.1.3 Overall system testing ...................................................................................... 112

8.2 Testing Strategy: Unit Testing ................................................................................ 113

8.2.1 Test case 01- Video Preview of main window ................................................ 113

8.3 Testing Strategy: Integration Testing ...................................................................... 114

8.3.1 Test Case: 01 Face detection and face extraction ............................................ 115

8.4 Testing Strategy: Overall System Testing ............................................................... 117

8.4.1 Test case: Overall System Testing ................................................................... 117

8.4.2 Conclusion of overall system testing ............................................................... 118

CHAPTER 9 .......................................................................................................................... 119

9 IMPLEMENTATION .................................................................................................... 119

9.1 Implementation face detection ................................................................................ 119

9.2 Implementation face extraction ............................................................................... 121

9.3 Implementing Training Set Manager ...................................................................... 122

CHAPTER 10 ........................................................................................................................ 123

10 CRITICAL EVALUATION .......................................................................................... 123

10.1 Limitations ........................................................................................................... 125

10.2 Suggestions for Future Enhancements ................................................................ 126

CHAPTER 11 ........................................................................................................................ 127

11 Conclusion ..................................................................................................................... 127

12 Bibliography .................................................................................................................. 128

13 Reference ....................................................................................................................... 131

14 APPENDICS .................................................................................................................. 134



14.1 Gantt chart ........................................................................................................... 134

14.2 User Manual ........................................................................................................ 142



LIST OF FIGURES

Figure 1: Data representation in ZN-Face. Such graphs can be shifted or .............................. 21

Figure 2: Add name tags in Picasa ........................................................................................... 22

Figure 3 : IPhoto Face Recognition ......................................................................................... 23

Figure 4: database of a person with different pose .................................................................. 24

Figure 5 : Face image in different illumination ....................................................................... 25

Figure 6: Sample occluded face images ................................................................................... 25

Figure 7 : Challenges in face recognition: images of actress ................................................... 26

Figure 8: Image representation in plane as matrix ................................................................... 27

Figure 9: Threshold based segmentation ................................................................................. 29

Figure 10 : Edge detection by using various methods ............................................................. 32

Figure 11: Masks used by Sobel Operator ............................................................................... 33

Figure 12: Masks used by Prewitt Operator ............................................................................ 34

Figure 13: Three commonly used discrete approximately to the Laplacian filter ................... 34

Figure 14: Original image with the result of various edge detection techniques ..................... 35

Figure 15: Author’s work based on Haar-features ................................................................... 40

Figure 16: Author’s work -non face detection ......................................................................... 42

Figure 17: Author’s work Negative results .............................................................................. 42

Figure 18: Integral image ......................................................................................................... 43

Figure 19: illustration of the integral image and Haar-like features ........................................ 43

Figure 20 : Different types of features ..................................................................................... 44

Figure 21: The basic Local Binary Pattern (LBP) operator ..................................................... 45

Figure 22: Basic scheme of AdaBoost –Authors work based on –Meynet (2003) .................. 47

Figure 23: the process of Neural Network Face Detection ...................................................... 49

Figure 24: Rectangle Features ................................................................................................. 52

Figure 26: This is not a face because F is small ...................................................................... 54

Figure 27: Eigenfaces obtained using ORL database .............................................................. 57

Figure 28: A training image as a weights sequence of Eigenfaces .......................................... 59

Figure 29: Diagram of Spiral ................................................................................................... 71

Figure 30: Structure of Waterfall Methodology ...................................................................... 72

Figure 31: Agile methodology life cycle ................................................................................. 74

Figure 32: Hybrid Model Structure.......................................................................................... 76

Figure 33: Dot Net Frameworks .............................................................................................. 79



Figure 34 : Use Case Diagram ................................................................................................. 90

Figure 35: Use Case Diagram for admin ................................................................................. 91

Figure 36 : Use Case for Admin and Criminal ........................................................................ 92

Figure 37: E-R diagram between administrator and the operator ............................................ 98

Figure 38: 7.4.2 E-R diagram between operator and the criminal ........................................ 98

Figure 39: Activity Diagram For System Administrator ....................................................... 100

Figure 40: Activity Diagram For Add Records ..................................................................... 102

Figure 41 :Activity Diagram for Operator Process ................................................................ 103

Figure 42: Sequence Diagram for Administrator ................................................................... 104

Figure 43 :Sequence Diagram for operator ............................................................................ 105

Figure 44: Collaborative diagram for Administrator ............................................................. 106

Figure 45 : Collaboration diagram for operator ..................................................................... 107

Figure 46: Component Diagram ............................................................................................ 108

Figure 47: Deployment Diagram ........................................................................................... 109

Figure 48: Class Diagram ...................................................................................................... 110

Figure 49: Code for Image Loading....................................................................................... 119

Figure 50 : Code for cascade object ....................................................................................... 120

Figure 51: Save detected faces in an array ............................................................................. 120

Figure 52: Final Result of face detection ............................................................................... 121

Figure 53: Extracted Faces ..................................................................................................... 121



LIST OF TABLES

Table 1: edge based segmentation ........................................................................................... 30 Table 2: User Interface Module-Admin Module ..................................................................... 64

Table 3: Admin Requirements ................................................................................................. 65 Table 4: Comparison between the hybrid model and spiral model ......................................... 77 Table 5: Use Case Description -Add New User ...................................................................... 93 Table 6 : Use Case Description For View Record ................................................................... 94 Table 7: Use Case Description of Train Image Space ............................................................. 95

Table 8 :Use Case Description -Find The Matched Image ...................................................... 96 Table 9 : System Architecture.................................................................................................. 97 Table 10: Test case 01: Video Preview Of Main Window .................................................... 113

Table 11: Test Case: 01 Face detection and face extraction .................................................. 115 Table 13 : Test case 01: Count Human Faces ........................................................................ 116 Table 14: Test case: Overall System Testing ......................................................................... 117



LIST OF ABBREVIATIONS

API - Application Programming Interface

GUI - Graphical User Interface

HSV - Hue, Saturation and value

IDE - Integrated Development Environment

OS - Operating System

OO - Object Oriented

SDK - Software Development Kit

UML - Unified Modelling Language

LDA - Linear Discriminant Analysis

OOP - Object Oriented Programming

PCA - Principal Component Analysis

SDLC - System Development Life Cycle

ATM - Automatic Teller Machine

2D - Two Dimensional

3D - Three Dimensional

WPF - Windows Persistence Language



Chapter 1

1 Introduction

1.1 Project background

In the modern world, security is a one of the main concerns. There is a significant rise of

threats to the society with increasing rate of crimes and terrorist activities. Even have many

ways of identifying a person, biometric identification approaches have had a huge attraction

because of the accuracy and the uniqueness of the biometric factors of a person. Finger print

recognition, voice recognition, palm recognition iris and voice recognition are the approaches

of biometric identification. The advantage of face recognition approach is people do not need

to look into an iris scanner or to place their hands on a fingerprint reader, or to speak to close

by microphone. And also face recognition techniques can be very useful in footages taken by

surveillance and applications.

This project is aimed to identify the criminal faces. In here the technique which going to use

is, manually we already store some images of the criminals in our database along with his

details. By surveillance camera system residing at some public place which automatically

matches the input faces with criminal database and gives alert if the results are matched. If

any image is matched up to 95% or closer to that rate then we predict that he is only the

criminal.

Face recognition technology can be applied to a wide variety of application areas including

access control for PCs, airport surveillance, private surveillance, criminal identification and

for security in ATM transactions. In addition, face recognition system is moving towards the

next-generation smart environment where computers are designed to interact more like

humans.



1.2 Understanding the problem

Take a face image of a person as an input. And compare the face image of a person with the

existing face images that are already saved in the database. So face detection is the main

problem here. When it come to the real world, it might not possible to capture a full frontal

picture of a face at all the times in uncontrolled environments. Even though there are many

face recognition application available, most of these applications work in optimal condition.

Detecting a face in probe image may be relatively simple task for a human. But it’s not for a

computer. The computer has to decide which pixels in the image is part of the face and which

are not. In a typical passport photo, where the background is clear, it’s easy to do, but when

the background becomes cluttered with other objects, the problem becomes extremely

complex.

As mentioned in the proposal there are many challenges specially uncontrolled background

and lighting, unrestricted range of facial expression and typical variations in hair style,

criminal wear specs or wig and difficult to identify facial marks ,aging, sketches and other

adornments. These variations have to handle by any face detection method which hopes to

operate in an uncontrolled environment.

Automated face recognition is a sequence of process as mentioned in below.

Capture or detect

images from a

video or from a

camera

Detect and extract

face area from the

image

Normalization

(Covert all

extracted faces in to

grey colour)

Match extracted

face features

against images in

database



By understanding the above, there are several sub steps need to be performed within the

steps. Even there are lot of approaches for full face recognition, the proposed solution

deviates from the regular face recognition approaches.

1.3 Project objectives

Main objective of this project is to identify a person using the images previously taken. The

identification will be done according the previous images of different persons.

Even today we use different types of face recognition applications sometimes those

applications also fail to identify faces because of different facial expressions and typical

variations in hair style. When the criminal where specs or wig it might get too difficult to

identify a face. Another thing is facial marks, like aging, sketches birthmarks or other

adornments will be big challenge to identify a face.

This face recognition system will identify individuals based on characteristics of separate

face segmentations and the objectives of the project as follows.

- Identify unique face features of eye, nose, and mouth region for recognises individuals.

-Improve capabilities of the detecting features of the local segmentations of face. So that it’s

necessary to find the efficient algorithms to extract features of the segmentations.

Through researches being carried on face recognition techniques and available algorithm on

partial face recognition and choose an appropriate method and implement face recognition

system based on it. Finally As there are many approaches to these contents, selection of the

most suitable techniques has to be determined after evaluating available techniques under

various factors.



1.4 Proposed solution

To overcome the drawbacks that were in the existing system we develop a system that will be

very useful for any investigation department. Here the program keeps track of the record

number of each slice during the construction of identifiable human face and calculate

maximum number of slices of the similar record number.

The proposed system takes segment of a face (eye region, nose region, mouth and forehead

region) at a time and identifies which has submitted region. Based on the input region it will

extract the features that are unique to each region. Then it will extract particular face region

form that saved in the database. After that system will match the features. Based on this

record number the program retrieves the personal record of the suspect

Proposed System

Face Tracking

Or Face

Detection

Image/ Video Face Detection

Face Localization,

Size and Pose

Face Extraction

Face Matching

Database of training

faces

Result

Aligned Face

Feature Vector



1.5 Project scope

The scope of the project is confined to store the image and store in the database. When a

person has to be identified the images stored in the database are compared with the existing

details. This project has narrow scope, after carrying out an extension research on face

recognition using an image or video stream. This project is concerned to cover following

area.

1.5.1 Accurate identification of faces

Identification of individuals those stored in the database. Here the technique is we already

store some images of the criminals in our database along with his details. And insert a photo

that we previously taken from a passport or somewhere else, and segmented into many slices

say eyes, hairs, lips, nose, etc so even when a criminal wear a wig or specs we don’t care

about it and it will not hard to identify criminals because from the system match other facial

features with the image what we insert to the system. Depend on the total matching weight

age, system predict that he is only the criminal.



Chapter 2

2 Domain investigation

2.1 Over View face recognition system

Computerized face recognition has been an active research area over the last 40 years. Also

manual face recognition systems had undertaken the interest of researches long ago. And

manual face recognition process is very slow to give the result. It is very critical to find the

criminal images.

Normally face recognition applications are used to recognize faces based on various factors

and technologies. Although today different face recognition systems use different approaches,

most of them perform key main steps, which are common to most face recognition

approaches.

Even the final result of recognize a face, but not all face recognition applications are same

when comparing with the process of steps. Some applications might be reliable than others,

depending on the quality of the image captured as well as the algorithms employed in the

application. There are many techniques used for facial recognition, mainly geometric and

photometric. In geometric technology especially data relies on shapes on the face called node

points (distance between the eyes, nose width, cheekbones and depth of eyes etc.).

Despite improvements, there is still no system available that can report with one hundred

percent accuracy. Despite the qualitative differences, all facial recognition software apply the

same process steps.

1. Face detection

2. Face Extraction

3. Face Normalization

4. Face Recognition



2.2 Similar systems

Face recognition has long been a goal of computer vision, but only in recent years reliable

automated face recognition has become a realistic target of biometric research. There are

several face recognition applications available in market and today most commonly use this

technology for security purposes.

2.2.1 FaceIT

IJCSIS [326], Indentix, a company based in Minnesota,is one of many developers of facial

recognition technology and FaceIT is one of a product of them.This is an award winning face

recognition software engine which allows computers to recognize faces rapidly and accurately

[13].this was also named as the top performer in the Facial Recognition Vendor Test (FRVT)

in the year 2002.

All the technologies which combine of face recognition including face detection, tracking,

segmentation and recognition are used in FaceIT.

Input images and

videos

Face Detection Normalizing the image if

needed (scaling and rotating)

Face extraction Face Recognition



Some of the measured by the software are:

Distance between the eyes

Width of the nose

Depth of the eye sockets

The shape of the cheekbones

The length of the jaw line

2.2.2 ZN-Face

This is an automated access control system developed by ZN Vision Technologies and this

application performs a biometric identification of persons from their facial images. To achieve

this they have applied various methods and several extensions. Mainly they used Elastic Graph

Matching for recognition human faces and it has proven to be a powerful algorithm.



Figure 1: Data representation in ZN-Face. Such graphs can be shifted or

As shown in above picture at every node within the graph, a feature vector is calculated from

the specific local information. The facial similarities between the live feature and graph model

are established on the basis of the weighted sum of the node similarities and possible to handle

large database that more than 1000 persons or more.



2.2.3 Google picasa

Google Picasa is combination with many features and the face detection and identification is

one of an interesting thing in picasa.

In Picasa there is an album called “unnamed people” and in this album where you can find

the faces that you haven’t named before. So you have to add a name of person for the

particular face image by manually.

After that when you upload an image to the Picasa, here after automatically it detects and

recognise the face and name that face by the name what we added earlier. Actually this is

very interesting.

The main advantage of using face detection in Picasa is that, once trained, the face detection

algorithm in Picasa works very well. Sometimes Picasa will find a face even if that face is

way off in the background somewhere too.

Figure 2: Add name tags in Picasa



2.2.4 APPLE IPhoto

(MacWorl, 2012) The face feature in iPhoto uses face recognition to help you find and name

the photos of your favourite people. When you upload a photo to iPhoto it automatically scan

your uploaded image and detect the faces and crop the only the faces and tell us to add the

names of people for the particular face. If it’s already added before, when you import new

photos, iPhoto scans them to see if any include people on your corkboard, and then suggests

selected photos for your confirmation.

Sometimes iPhoto detect non faces too.

Figure 3 : IPhoto Face Recognition



2.3 Problem of the face recognition systems

2.3.1 Pose variance

(Ramchandra, 2013) Normally even in face recognition system used frontal view face images

of individuals as training data. In a frontal view images have more specific and critical

information of a face than other pose angle images. The problem appears when the system

has to recognize a rotated face using this frontal view training data. User needs together

multiple views of an individual in a face database. Most difficult case in pose problem is

when training image pairs are not.

2.3.2 Lighting/illumination variance

(Ramchandra, 2013) The primary requirement of the face recognition system is quality face

image and good quality image. Sometimes due to changing in lighting, same face might be

appear in a differently. Drastically a appearance of an object can be change by the

illumination. Following image processing can be used to provide a degree of lighting

invariance.

Normalizing

Histogram equalization

Order statistic filtering

Figure 4: database of a person with different pose



Heuristic, light-modelling, model based and statistical approaches are some approaches in

illumination problems.

2.3.3 Different facial occlusions

Partial face occultation is one of the challenging problems in face recognition. A face

recognition system can face with the occulted faces in real life. Mostly due to use of

accessories such as scarf or sunglass, hands on the face or cover the face with the object that

person carry. There for a face recognition system has to be robust to occlusion in order to

guarantee reliable real world operation.

Figure 5 : Face image in different illumination

Figure 6: Sample occluded face images



2.3.4 Makeup challenges

Another big challenge of face recognition is makeup or plastic surgery and it may reduce the

accuracy of the system. Even accuracy of the face recognition systems has rapidly increase in

past decades there are additional factors like biological change, and plastic surgery, a

medically induced change and what we discussed above compromise the use of these system

in security applications. (See Figure 7 and Figure 8)

The impact of cosmetic makeup is a big challenge on automated face recognition system. But

today use some special algorithms to reduce the impact from biological changes.

Figure 7 : Challenges in face recognition: images of actress

Jennifer Grey demonstrating variations due to pose, illumination, expression, and alterations

through plastic surgery, aging and makeup (Spie, 2015)



Chapter 3

3 System research and investigation

3.1 Fundamental of image processing

Digital image processing can be considered as one of the interesting area of the computer

vision and it’s a method to convert an image into digital form and perform some operation on

it. Basically image processing system based on concepts on mathematics and includes

treating images as two dimensional signals. Analog and visual techniques are the methods

that used in image processing.

Today image processing is rapidly growing technology in the computer industry. Following

section shows fundamental of image processing.

3.2 Image classification

According to Jahne (2005, pp32) there are entirely different ways to display an image. The

most popular way to represent an image is rectangular grid. Gonzales (2004, pp 01) further

described it as follows, digital images can be represent as a matrix as it can represent as 2D

function where x and y gives coordinates in geometric plane.

Figure 8: Image representation in plane as matrix Source: [Johne, 2005, pp32]



3.3 Image segmentation

Image segmentation is the division of an image into meaningful structures and it’s very

important step in image processing such as object representation, visualization and many

other image processing tasks. Not only today, in the past decades various types of image

segmentation methods have been proposed, such as:

Threshold based segmentation

Edge based segmentation

Region based segmentation

Clustering techniques

matching

In this chapter the author has discussed few techniques of image segmentation that related to

face recognition process.

According to Grady & Schwartz (2006) “Image segmentation has often been defined as the

problem of localizing region of an image related to content”

According to Biswas (2008) image segmentation approaches can divide as two different

categories as follows.

Segmentation approaches

Pixel based segmentation

Region based segmentation

Edge based segmentation

Segmentation algorithms for images generally based on the similarity of image intensity

values.



3.3.1 Threshold based segmentation

(John Ashburner & Karl J. Friston) Threshold based segmentation method is the most

frequently used technique to segment an image, and the grey value of thresholding operation

remapping the operation of g defined by :

G (v) = {0 𝑖𝑓 𝑣 < 𝑡1 𝑖𝑓 𝑣 > 𝑡

Figure 9: Threshold based segmentation

(John Ashburner 2011) An original image is shown at the top left. If it is known that this

image contains only uniformly sized squares, then the image on the top right shows the

correct segmentation.

Each segment has been indicated by a unique grey value here. The bottom left and right

images show examples of over segmentation and under segmentation respectively.



3.3.2 Edge based segmentation

Image segmentation can also be done by edge detection techniques. In the edge detection

technique mainly identify the boundary of an image. Edges are detected to identify the

discontinuities in the image. In here edges on the region are traced by identifying the pixels

value and it is compared with the other closer pixels and no need to detect edges to be

closed.

Basically there are two steps for edge based segmentation

Edge detection for edges of pixels – Gradient, Laplacian and Canny filtering are the

techniques use for edge detection.

Edge linking- Linking adjacent edges in to edges

The algorithms what use for edge base segmentation are less complex. And the edges of an

object are important feature in an image to separate the regions. But sometimes region can

often be hard to find due to noise or occlusions.

Table 1: edge based segmentation



3.3.3 Edge detection

(Rashmi, 2004) An edged can be defined as a set of connected pixels that forms a boundary

between two disjoints regions.Edge detection is a basic tool used in image processing,

basically for feature detection and extraction, which aim to identify points in a digital image

where brightness of image changes sharply and find discontinuities.

The major property of the edge detection technique is its ability to extract the exact edge line

with good orientation. Edge detection is the fundamental tool for image segmentation. An

edge detection method transform original images in to edge images and it’s a fundamental

process detects and outlines of an object and boundaries among object and the background in

the image. Today edge detection is used for object detection which serves various

applications like medical image processing, biometrics etc.

Edge detection

Gradient

LoG

Canny

Sobel

Robert

Laplacian



In edge detection, basically get the boundary of an image or contour at which a significant

occurs in some physical aspects of an image. Edge detection is a well-developed field on its

own within image processing. Edge detection is basically image segmentation technique,

divides spatial domain, on which the image is defined, into meaningful parts or regions.And

there is different ways to perform edge detection. Especially this edge detection can

categories in to two categories, like gradient and Laplacian. Below figure shows the edges of

an image detected using the gradient methods of Robert, Perwitt and Sobel and the Laplacian

method of Marrs-Hildreth.

Figure 10 : Edge detection by using various methods



3.3.3.1 Canny’s edge detection

(R.Yogamangalam) The purpose of Canny’s edge detection is to significantly reduce the

amount of data in an image, while preserving the structural properties to be used for further image

processing.

Here is the Canny’s Edge detection procedure that runs in 5 separate steps:

According to IJET (R.Yogamangalam 2011)

1. Smoothing: To reduce the effect of noise, the surface of the image is smoothened by

using Gaussian Convolution.

2. Finding gradient: The edges should be marked where the gradients of the image has

large magnitudes.

3. Non- maximum suppression: Only local maxima should be marked as edges.

4. Double thresholding: Potential edges are determined by thresholding.

5. Edge tracking by hysteresis: Final edges are determined by suppressing all edges

that are not connected to a very certain (strong) edge.

3.3.3.2 Sobel edge detection

(Radha, 2011) Sobel edge detection method is introduced by Sobel in 1970 and the Sobel

edge detection is initially used for edge detection but they did not give sharp edges and were

highly sensitive to noise image. This method used to find the estimated absolute gradient

magnitude at each point in n input greyscale image. To detect edges along the horizontal and

vertical axis, the Sobel kernels are most suitable for that and Robert’s able to detect edges

along the vertical axis of 45°and 135°

Figure 11: Masks used by Sobel Operator



3.3.3.3 Prewitt Edge Detection

(Muthukrishnan, 2011) The Prewitt edge detection is proposed by Prewitt in 1970 to estimate

the magnitude and Prewitt is the correct way to estimate the orientation of an edge. This

gradient based edge detector is estimated in the 3x3 neighborhood for eight directions. All the

eight convolution masks are calculated. One complication mask is then selected, namely with

the purpose of the largest module. Prewitt detection is slightly simpler to implement

computationally than the Sobel detection, but it tends to produce somewhat noisier results.

3.3.3.4 Laplacian of Gaussian

(Maini, 2011)The Laplacian is a 2-D isotropic measure of the 2nd spatial derivate of an image.

The Laplacian of an image highlights regions of rapid intensity change and is therefore often

used for edge detection. The operator normally takes a single gray level image as input and

produces another gray level image as output.

Figure 12: Masks used by Prewitt Operator

Figure 13: Three commonly used discrete approximately to the

Laplacian filter



3.3.3.5 Justification of edge detection techniques

In this section the author has presents the relative performance of various edge detection

techniques what we discussed above and other techniques. The performance of the edge

detector is compared to commonly used or comparable algorithms such as the Canny Sobel

and Robert’s edge detection algorithms

Roberts, Sobel and Prewitt results are actually deviated from the others.

Marr-Hildreth, LoG and Canny edge detection almost same results.

Kirsch and Robinson edge maps are almost same.

But Canny’s edge detection is the superior when comparing with the other techniques.

Even though so many edge detection techniques are available in the computer science field,

since it a challenging task to detect the exact image without noise from the original image.

Figure 14: Original image with the result of various edge detection techniques



3.4 Face detection approaches

(Clare Tischer 2012) Face detection is the computer vision technology and that detects or

determines the location and the size of a face in image that human can do effortlessly.

Anyhow when comparing with the computer vision terms, this task is not easy. A face

detection system should be able to achieve the regardless of illumination, orientation or

camera distance. And also there are general problems in face detection system such as

extraction and verification of faces, possibly identify only faces or face features from an

uncontrolled background.

Detection will happen based on the following techniques

Face Detection

Feature-based

Image-Based

Low-Level

Analysis

Feature

analysis

Edges

Colour

Motion

Constellation

analysis

Linear subspace methods

Linear subspace methods

Statistical approaches

Author’s work based on Article of Luis Torres’s 2012



3.4.1 Controlled environment

It’s the important and straightforward case. Normally photographers are taken under

controlled light, background etc, so in that case can use simple edge detection techniques to

detect faces.

3.4.2 Colour images

To detect faces, typically skin colour can be used to detect faces. And the result might be

change or weak if the light conditions change. Human skin colour can be change from nearly

white to almost black. (Proyecto 2010) The major difference lies between their intensity, so

chrominance is a good feature. It’s not easy to establish a solid human skin color

representation. However, there are attempts to build robust face detection algorithms based

on skin color

3.4.3 Images in motion

Detect faces in a real time videos is a challenge. There is a continuing challenge to achieve

the best detecting results with the best possible performance. (Proyecto 2010) Another

approach based on motion is eye blink detection, which has many uses aside from face

detection



3.4.4 Face detection difficulties

It’s very hard process to build automatic robust face detection. Because, even a face detection

system detect faces in different image situations and different face condition it seems really

easy to do this with the human visual system. In fact the object of “face” is hard to define by

mathematical algorithms, as its large variability depending on the identity of the person, the

lighting conditions and the psychological context of the person etc.

The main challenge for detecting faces to is to find a classifier which can discriminate faces

from all other possible images.

When we consider face as a global attribute, we can extract some common attributes

from every faces. Normally even we consider face as an ellipse object but there can be

thin faces and also rounder faces. And the skin colour also different from person to

person. So a system might get confuse to detect faces automatically.

The pose of the face: by the position of a person where he stands in front of the

camera might get change the view of the faces.

The facial expression: because of face appearance the results might get change. The

face feature of a smiling face can be far from the appearance of a sad face.

Presence of added objects: the objects that we can find usually on a face can be

affecting the result of detecting a face. If a person wear a sunglass, that will change

one of a main characteristic of a face. And also from the natural facial features such as

beards and hair style which can occult one part of a face.

Image condition: the appearance of a face might get change to one another due to

light conditions so illumination and the intensity have to be considered.



3.5 Face detection methods

Today face detection and face feature recognition has been an active research area in the

computer vision field. (Zhang, 2010)Face detection is the basic and the first step of face

recognition. A number of methods and algorithms have been proposed for face detection and

these methods can group in to several categories like

Knowledge based methods use predefined rules to determine a face based on human

knowledge.

Feature based approaches main target is to find the features of a face structure.

Template matching methods use some sample face templates to judge if an image is a

face.

Appearance based methods get an idea of a face models from a set of representative

training face images.

In this technical report, author survey the recent advances in face detection for the past

decade and the Viola-Jones face detector is first re-viewed and In feature invariant

approaches usually consider about skin color, texture and facial features. Then survey the

various techniques according to how extract features and what learning algorithms are

adopted and hope to review several existing algorithms.

Face Detection

Knowledge based

methods

Template

matching methods

Feature invariant

approaches

Appearance based

methods

Author’s work based on Technical Report of Zhang, 2010



3.5.1 The Viola-Jones Face Detector

(Zhang, 2010) Face detection proposed by Viola and Jones based on statistic methods is most

popular among the face detection approaches. This face detection is a variant of the

AdaBoost algorithm which achieves rapid and robust face detection. In AdaBoost algorithm

is proposed a face detection method based on AdaBoost algorithm using Haar Features that

detected the face successfully with high accuracy. But still we can’t satisfy with the accuracy

of this method.

The basic principal of the Viola-Jones algorithm is to scan a sub window that capable of

detecting faces across the given image. The standard image processing approach would be to

rescale the input image to different sizes and then run the fixed size detector through these

images. Rather than time consuming due to the calculation of the different size images, this

approach is better. But in Viola-Jones has a method of scale invariant detector that has the

same number of calculations whatever the size. This detector is constructed using a integral

image that can be used to compute simple Harr-like rectangular features.

The propose method to detect face is Viola Jones Haar classifier cascade and it draws a

rectangle around faces as shown below.

Figure 15: Author’s work based on Haar-features



3.5.1.1 Advantages and disadvantages of Viola-Jones Technique

Advantages

Viola Jones face detection algorithm is the most admired algorithms for face detection

in real time.

Another main advantage is uncompetitive detection speed.

When comparing with other face detection algorithms Viola Jones is an especially

successful method because it has a low false positive rate.

Disadvantages

Actually its takes long training time

Limited head poses

Not detect black faces (depend on the photo background)

Furthermore in Viola Jones they have used three types of features. Two rectangle features,

three rectangle features and four-rectangle features which is respectively the difference

between the sums of the pixels within the two rectangular region.



3.5.1.2 Generating negative values

Some times for the training images the cascade classifier generates negative values or detects

non faces too. A negative example is basically just an image not containing faces. Below

figure 16 shows how the cascade classifier detects non faces.

The negative examples should be generated in the same manner as the detector scans through

an image. This means that negative examples should be generated at every size and then

rescaled to the base resolution of 24*24 pixels. It detects all faces within given image via

Haar classifier cascade and draws rectangle around them.

(Lyngby, 2008) Haar-like [9], [14], [15] feature classifier cascades are composed of multiple

classifiers, or conditions, that are used to distinguish unique objects (i.e., face, eye, etc.) from

anything that is not that object. Classifiers are a set of values representing sums of pixel

brightness on a region-by-region basis. These specifically structured regions are called Haar-

like features. Haar-like feature classifiers are created using the integral image which is an

intermediate image representation that allows the features used by the detector to be

computed very quickly. Rectangle features can be computed very rapidly using the integral

image.

Figure 16: Author’s work -non face detection

Figure 17: Author’s work Negative results



3.5.1.3 The scale invariant detector

First step of the Viola-Jones face detection algorithm is to turn the input images in to integral

image. This is done by making each pixel equal to the entire sum of all pixels above and the

left of the concerned pixel. This demonstrate in Figure 16

This allows for the calculation of the sum of all pixels inside any given rectangle using only

four values. These values are the pixels in the integral image that coincide with the corners of

the rectangle in the input images. This demonstrates in figure 17.

3.5.1.4 The Integral image

Each pixel in the integral image represents the change in brightness of the corresponding

pixel in the original image by finding the sum of the derivation of the pixels above and left of

the original

Figure 18: Integral image

B A

C D

1 2

3 4

Figure 19: illustration of the integral image and Haar-like features



Sum of the ABCD rectangle =4+1-(2+3)

1= A

2=B+A

3=A+C

4 = B+D+C+A

Sum of the ABCD rectangle =B+D+C+A+A-(B+A+A+C)

=B+D+C+A+A-B-A-A-C

=B+D+C+2A-B-2A-C

= B+D+C+2A-B-2A-C

= D

Now it has been demonstrated the sum of pixels within rectangles of arbitrary size. The

Viola-Jones face detector analyzes a given sub window using features consisting of two or

more rectangles. The different types of features are shown in figure

Figure 20 : Different types of features



3.5.2 Local Binary Patterns (LBP)

(THÈSE N, 2006)The LBP operator is a non-parametric 3*3 kernel which summarizes local

special structure of an image. It was found by Ojala. The Local Binary Pattern technique is

very effective to describe the image texture features.LBP has advantages such as high speed

computation and rotation invariance, face recognition image segmentation etc.

According to the THÈSE N, 2006, [17] even in early the cascade quickly discard most of the

background regions, in last stages of the cascade, a large number of Haar-like features are

necessary to reach the desired detection acceptance rate trade off. [18] It results in a long

training procedure and cascade with several dozen stage which are difficult to design. So

furthermore Haar like features are not robust to local illumination changes.

3.5.2.1 Frontal face detection using LBP

Figure 21: The basic Local Binary Pattern (LBP) operator

(Varsha, 2014)Very recently, the LBP was successfully applied to the detection of moving

objects via background substation. In LBP, every pixel is assigned a texture value, which can be

naturally combined with target for tracking thermo graphic and monochromatic video. The major

uniform LBP patterns are used to recognize the key points in the target region and then form a

mask for joint color-texture feature selection. [6695]



3.5.2.2 Advantages and disadvantages of LBP

Advantages

(According to the Sharma, 2014)

Effective to describe image texture features.

By the background subtraction detect the moving object.

It’s a simple approach

Computationally simple than Haar-like features and fast

The most properties of LBP feature are tolerance against the monotonic illumination

changes and computational simplicity.

Disadvantages

(According to the Sharma, 2014)

Not sensitive for small changes in the face localization

When using large local regions, increases the number of errors

It’s insufficient for non-monotonic illumination changes

Not accurate

Use only binary and grey images.



3.5.3 AdaBoost Algorithm for face detection

(Meynet, 2003)AdaBoost algorithm is highly inspired by the Robust Real time object

detection of Viola and Jones. Boosting algorithms is a powerful iterative procedure that

builds efficient classifiers by selecting and combining very simple classifiers. This new

method given by Viola [1] is a combined method of more traditional ones like geometrical

image based detection that is a geometrical in the sense that it uses general features of human

faces.

(Varsha Gupta, 2014) AdaBoost algorithm was the first practical Boosting algorithm and one

of the most widely used and studied with applications in numerous fields. [2] AdaBoost is a

learning algorithm which produces a strong classifier by choosing visual features in a family

of simple classifiers and combining them linearly. Although AdaBoost [12] is more resistant

to over fitting than many machine learning algorithms, it is repeatedly sensitive to noisy data

and outlier’s. [2] AdaBoost is called adaptive because it uses multiple iterations to generate a

single composite strong learner.

In AdaBoost algorithm, it has two main goals.

Selecting a few set of features which represents as well as possible faces.

Train a strong final classifier with a linear combination of these best features.

Figure 22: Basic scheme of AdaBoost –Authors work based on –Meynet (2003)

AdaBoost

Training Set

Family of weak classifiers

1 strong

classifier



3.5.3.1 Advantages and disadvantages of AdaBoost

According to the (Gupta, 2014)

Advantages:

AdaBoost algorithms need only two inputs: A training dataset and the set of features

and there is no need to have any prior knowledge about face structure.

Very simple to implement

Do feature selection resulting in comparatively simple classifier fast

Simple and easy to program

Disadvantages:

The quality of the final detection depends highly on the consistence of the training set.

Quite slow training

Weak classifiers too complex leads to over fitting

Very sensitive to noisy data and outlier



3.5.4 Neural Network based face detection

Artificial Neural Network is one of a popular tool in face recognition. In here the technique

what uses in Neural Network is pattern recognition and classification. Kohonen [11] was the

first to demonstrate that a neuron network could be used to recognize aligned and normalized

faces. [9] A rationally attached neural network examines small windows of an image, and

chooses whether each window contains a face. [12] Training a neural network for the face

detection task is challenging because of the difficulty in characterizing prototypical none face

images. [12] Unlike face recognition, in which the classes to be discriminated are different

faces, the two classes to be discriminated in face detection are “images containing faces” and

“images not containing faces”.[12] It is easy to get a representative sample of images which

contain faces, but it is much harder to get a representative sample. Figure 23 shows this

approach.

Figure 23: the process of Neural Network Face Detection



3.5.4.1 Advantages and disadvantages of Neural Network

Advantages

This algorithm can detect between 78.9% and 90.5% of faces in a set of 130 test

images with a satisfactory number of false faces detections.

Less computationally expensive

Acceptable false detection

Disadvantages

The detection process is slow due to train the non-face window.

The result is not so much accurate

A number of guidelines for future effort

The methodology is complex



3.6 Face Region extraction approaches

The face recognition approach was introduced by Sir Francis Galton in 1888 (Kepenekci,

2001).which was done by measuring four characteristics of French prisoners. Because of that

attempt of Sir Francis Galton to identify persons in more scientific way, it made foundation

of the biometric recognition which cause to improvement of face recognition.

The author has identified several approaches to extract face features during the research.

Among them following techniques show promising results.

3.6.1 Template matching

(Adaptive-vision 2007-2014) Template Matching is a high-level machine vision technique

that identifies the parts on an image that match a predefined template. Advanced template

matching algorithms allow finding occurrences of the template regardless of their orientation

and local brightness. Template Matching techniques are flexible and relatively

straightforward to use, which makes them one of the most popular methods of object

localization.

(Proyecto, 2010) Most of face recognition algorithms are combination with some template

matching techniques. Basically in here the process of template matching uses pixels, samples,

models or textures as patterns. The recognition function computes the different between these

features and the stored templates. Even the matching of 2D images was the early trend

nowadays 3D templates are common.



3.6.2 Haar like features-Viola Jones

Like we discussed in previous topics Viola-Jones (2001) approach they have used features

that generated by Haar basis functions. According to Viola Jones (2001) the reason for using

features are “that features can act to encode ad-hoc domain knowledge that is difficult to

learn using a finite quantity of training data.” Further more in this topic have discussed how

to use an integral image that we mentioned in previous topic to feature extraction.

3.6.2.1 Integral image for feature extraction

Rectangle Feature value of F=∑ (pixels value in white area)-∑ (pixels values in dark area)

Figure 24: Rectangle Features

[Source: Viola & Jones, 2001



1 4 5 2

3 0 1 3

5 5 4 1

0 3 2 5

Find the rectangle feature value of the box enclosed by the dotted line

Rectangle Feature value of F=∑ (pixels value in white area)-∑ (pixels values in dark

area)

= (5+4)-(0+1)

=9-1

=8

A simple face detection method using one feature

Rectangle Feature value of F=∑ (pixels value in white area)-∑ (pixels values in dark

area)

So

Pseudo code for detection method is:

If (F) is larger then it’s a face

If (F)>threshold, then

Face

Else

Non-face



The purpose of find the sum of pixels values

Figure 26: This is not a face because

F is small Figure 25: This is a face, because

the eye-area is dark, the nose

region (white area is bright) so F

is large. Then this can take as a

face

B

A

B

A

C D

F

G

E

H

You may consider these features are face

features:

Left eyes: (Area _A – Area _B)

Nose: (Area _C + Area _E – Area_ D)

Mouth: (Area _F + Area _H – Area_ G)

Authors work based on: Wong 2004



3.7 Face recognition Methods

The five main algorithms of face recognitions are

Eigenfaces

Fisherfaces

Laplacianfaces

Elastic Graph Matching

Neural Network

Vector mechanism

In order to develop a useful and applicable face recognition system several factors need to be

taken in hand

1. The overall speed of the system from detection to recognition should be acceptable.

2. The accuracy should be high

3. To increase the number of subjects, the system should be easily updated and enlarge

All face recognition methods can be classified as either 2D or 3D. The distance between

important points where used to recognize known faces, e.g. measuring the distance between

the eye or other important points or measuring different angles of facial components.

Techniques

Appearance based

techniques

Feature based techniques Model Based techniques

Eigen Faces

And Fisher Faces Distance between

landmark points such as

eyes, nose and mouth.

Graph matching

techniques

Active appearance / shape

models, fitting morph able

models

Author’s work based on Cybula 2004



Face recognition is such a challenging yet interesting problem that it has attracted researches

that have different backgrounds: Psychology, pattern recognition, neural networks, and

computer vision and computer graphics.

The following methods are used to face recognition.

1. Holistic Matching Methods (appearance)

2. Feature based methods (landmark)

3. Hybrid methods

Feature classifier and get the distance between different faces are the main challenges.

3.7.1 Holistic matching methods

In holistic approach, the complete face region is taken in to account as input data into face

catching system. One of the best examples of holistic methods are Eigenfaces and Fisherfaces

that most widely used method for face recognition and use Principal Component Analysis,

Linear Discriminate Analysis and independent component analysis for face recognition.

In those two approaches face images are kept as templates in the database and matched

against the image to be identified.

3.7.2 Eigenfaces

The process of facial recognition is discriminating input image data into several classes. The

input signals are highly noisy caused by different lighting conditions, pose etc. still input

images are not completely random and in spite of their differences there are, there are

patterns which occurs in any input signal. The characteristics of eyes, nose and mouth in any

face and that has relative distances between these characteristics. The characteristics features

are called Eigenfaces in the facial recognition.



These characteristics can be extracted out of original image data by means of a mathematical

tool called Principal Component Analysis (PCA) also known as Karhunen-Loeve methods,

and it’s a technology now commonly used for dimensionality reduction in computer vision

and choose a dimensionality reducing linear projection that maximizes the scatter of all

projected samples.

At elementary level, the image of a face can be expressed as a one dimensional column

vector of concerted rows of pixels.

X= [x1, x2, x3……….. xn]

Where n is the total number of pixels in the image. To compare two images, the simplest

method is to compare the image pixels by pixel. But n might be large for such comparison.

For an example n would be 10000 for a small image with 100*100 pixels. So comparison

might be time consuming and not efficient.

According to the above scenario, as most of them pixels do not represent the feature of face,

not all pixel values are important for comparison. Because of that reason dimensional

reduction technique, PCA is engaged in Eigenfaces approaches for keep the representation of

image compact and efficient for comparison.

Figure 27: Eigenfaces obtained using ORL database



3.7.3 Principal component analysis

Principal Component Analysis also known as Karhunen-Loeve (KL) as mentioned above and

as well as Hotelling Transform In image processing that is one of the most used and cited

statistical method for feature extraction. It’s a technique of identifying patterns of data and

expressing the data in a way to highlight similarity and differences.

Consider that have set of 15 images (M) as shown above (Figure 1), each image is

transformed into a vector of size N and placed into the set.

S= {X1, X2, X3.............................., Xm}

After obtain the set, will obtain the image Ψ

Ψ = 1

𝑀 ∑ 𝑋𝑛

𝑀𝑛=1

Get the value of differences between the input image and the mean image

𝛷𝑖 =𝑋𝑖- Ψ

Eigenfaces works by finding the eigenvectors and Eigen values of covariance matrix C from

the training set of m images. Find the average face µ𝑘of the set which is defined by:

µ𝑘=1

𝑀∑ 𝑋𝑖

𝑀𝑛=1

And calculate the covariance matrix as follow

C=1

𝑀∑ (𝑋𝑖 − 𝜇𝑘)𝑀

𝑖=1 (𝑋𝑖 − 𝜇𝑘)𝑇

A new face is transformed in to Eigenfaces component and after compare input image with

mean image and multiply their differences with each eigenvector of the L matrix. Each value

would be represent a weight and would be saved on a vector

Y= Wpea(X-µk)



Where Y= Wpea [𝑦1, 𝑦2, … … … … . . 𝑦𝑑]𝑇is the feature vector of the image that contains

weights to describe the contribution of each Eigenfaces in representing the input image X.

Each training image can be represented by a weighted sequence of Eigenfaces, as shown

below.

Figure 28: A training image as a weights sequence of Eigenfaces



3.7.4 Fisherfaces

Belhumer et al. [29] Fisherfaces method is an example of a class specific method suggested

in 1997. And it’s more reliable method for reducing the dimensionality of the feature space.

Fisherfaces use the same template matching method similar to Eigenfaces that we described

earlier and use a projection of images into a feature space. The difference is that the

Fisherfaces uses the Fisher’s Linear Discriminant (FLD) [37] [47] where the Eigenfaces uses

the Principal Component Analysis (PCA).

3.7.4.1 Comparison Eigenfaces to Fisherfaces

Fisherfaces Eigenfaces

Computational complexity

Slightly more complex

Simple

Effectiveness across pose

Good, even with limited

data

Some, with enough data

Sensitivity to lighting

Little

very

Anyhow both Fisherfaces and Eigenfaces techniques work very well for a uniformly and

densely sample data set varied over pose. When sparser data set across pose is available, the

Fisherfaces approach performs better than Eigenfaces.



3.7.5 Template matching face recognition methods

(Marqu, 2010) Template matching techniques uses in many face recognition algorithms. A

template matching process uses pixels, samples, models or textures as pattern. [13] The

recognition function computes the differences between these features and the stored

templates. It uses correlation or distance measures.



CHAPTER 4

4 REQUIRMENTS SPECIFICATION

4.1 System requirements

Since this system is about the identifying individuals using partial, face recognition, this

system will have simple interfaces to interaction with users. In functional requirements

section it will shows the procedures of the proposed system and what will be the functional

outcome of the system. Then under non-functional requirements, it will describe the

performance, usability and security of the system.

4.1.1 Functional requirements

By conducting the requirements analysis we listed out the requirements that are useful to

restate the problem definition.

Face detection

- The user can detect the faces of the image that the user entered to the system

Manual face detection

- To increase the accuracy of the face detection , the user add a function to detect

faces manually when the system not detect faces automatically.

Extract the detected faces

- After detect the faces from an inserted image, the user can extract only the faces

from the image.

Face recognition

- Using this modular user will able to input probe image to search in database after

allows recognizing inputted image with the image in the database. And identify

the face by using face features one by one and get the weight age of the matching

face image and identify the face by results of the total weight age.



Module Description

Author structured designs improve the maintainability of a system. A structured system is one

that is developed from the top down and modular, that is, broken down into manageable

components. In this project the author modularized the system so that user have minimal effect

on each other. This application is designed into five independent modules which take care of

different tasks efficiently.

1. User Interface Module.

2. Admin Module.

3. Client Module.

4. Database Operations Module.

5. Identify Module.



User Interface Module:

- Actually every application has one user interface for accessing the entire

application. In this application also we are providing one user interface for

accessing this application. The user interface designed completely based on the end

users. It is provide friendly accessing to the users. This user interface has attractive

look and feel. Technically I am using the C# for preparing this user interfaces.

Admin Module:

User requirements Elaboration Further Elaboration

Create Assign new user id &

password for an employee.

Delete Administrator can delete

the user id & password of

unwanted employee.

Update First the details of

criminals are to be

obtained by using CrimeID

Table 2: User Interface Module-Admin Module



Client Module:

Database Operations Module:

ADD MODULE:

The add module is helpful in adding the details of the criminals along with the details of the criminal

photo. While adding the details of the criminal, we crop the image of the criminal and store those

cropped parts in a separate database.

DELETE MODULE :

This module deletes the criminal details along with the photo. The operator first submits the criminal

id and searches for the availability of the id in the database. If that id is available in the database, then

the operator may delete the record of that particular criminal.

User requirements Elaboration Further Elaboration

Login User log in to home page by

entering id & password.

Adding details Personal details of criminal

store in to data base

Images are cropped and saved in

database.

Update process Enter criminal id and obtain his

details

Update the details and images of

existing criminal

Delete process Enter criminal id Delete the details and image of

unwanted criminal

Logout Logout in to the home page

Table 3: Admin Requirements



UPDATE MODULE:

The operator first enters the criminal id and searches for the availability of that id .If that id is available

in the database , then the details of that criminal are retrieved and the operator can update the details of

that criminal and that updated details of the criminal are stored in the database again for future retrieval.

Identify Module:

The cropped parts of the criminals, along with the criminal Id are viewed by the eyewitness .The

eyewitness selects particular cropped part of the criminal and it is freeze by the operator., then complete

face of the criminal is constructed and the details of the criminal is retrieved.

4.1.2 Non-functional requirements

Accessibility

- Since this developed as PC client application , this will able to access only single

user at a time

Security

- Login module will be implemented for user login

4.2 Resource requirements

This part is done by based on technical research and investigation which is describe in

Chapter 6.

Under hardware requirements, it considered minimum hardware requirements that need to

develop this application and run the developing software.

In software requirements section, it will talk about the software requirements that need to

implement this application.



4.2.1 Hardware requirements

Microsoft Visual Studio 2012 required at least 1.73 GHz CPU, 515 MB RAM, 64 bit

operating system and 515 VGA

Emgu CV requires Visual Studio 2012 with windows operating system as minimum

requirements.

It is suggested following minimum hardware requirements

The computer must have the following minimum capabilities.

(According to MSDN corporate website, 2014)

32-bit (x86) or 64-bit (x64) processors

Dual-core, 2.66-GHz or faster processor

USB 2.0 bus dedicated to the Kinect

2 GB of RAM

Graphics card that supports DirectX 9.0c

Recommended Hardware for Kinect

[11] Desktop PC with 3GHz (or better) multi-core processor and a graphics card with 2GB or

more of dedicated on-board memory.[11] Kinect Fusion has been tested for high-end

scenarios on a NVidia GeForce GTX680 and AMD Radeon HD 7850.

Supported architectures

32-bit (x86) (Windows 7 and Windows 8 clients)

64-bit (x64)

Hardware requirements for windows 7/windows 8

2.2 GHz or faster processor

1 GB RAM – Basic installation



4.2.2 Software requirements

Following minimum software requirements need for implementation of the solution

Visual Studio 2010 or Visual Studio 2012. The free Express editions can be

downloaded from Microsoft Visual Studio 2010 Express or Microsoft Visual Studio

2012 Express.

.NET Framework 4 (installed with Visual Studio 2010), or .NET Framework 4.5

(installed with Visual Studio 2012).

Emgu CV 2.4

Windows xp or windows 7/windows 8



CHAPTER 5

5 DEVELOPMENT METHODOLOGY

This document play a vital role in the development of life cycle (SDLC) as it describes the

complete requirement of the system. It means for use by developers and will be the basic

during testing phase. Any changes made to the requirements in the future will have to go

through formal change approval process.

There are different approaches to develop particular solution. However, the approach

should select based on the characteristics and requirements of the project.

The characteristics of the project can be listed down as follows

Project is short terms and will be deployed by an individual.

The functional and non-functional requirements of the artefact can be categorised

based on modules.

Managed the limitations

Needed to change or modify the functionalities and features

The next section of the chapter briefly explain some of development approaches analysis

them to select best approach for the project



5.1 Spiral model

(Dr. Barry, 1988) SPIRAL MODEL was defined by Barry Boehm in his 1988 article, “A

spiral Model of Software Development and Enhancement. This model was not the first

model to discuss iterative development, but it was the first model to explain why the

iteration models. [14].

As originally envisioned, the iterations were typically 6 months to 2 years long. [14] Each

phase starts with a design goal and ends with a client reviewing the progress thus far. [14]

Analysis and engineering efforts are applied at each phase of the project, with an eye toward

the end goal of the project.

The steps for Spiral Model can be generalized as follows:

(According to Tutorials-Point, 2015)

The new system requirements are defined in as much details as possible. [12] This

usually involves interviewing a number of users representing all the external or

internal users and other aspects of the existing system.

A preliminary design is created for the new system.

A first prototype of the new system is constructed from the preliminary design. This

is usually a scaled-down system, [12] and represents an approximation of the

characteristics of the final product.



5.1.1 Diagram of spiral

(According to the Tutorials_Point, 2015) [13]

5.1.2 Advantages of spiral

High amount of risk analysis there for avoidance of Risk is enhanced.

Good for large and mission critical projects

Strong approval and documentation control

Additional functionality can be added at later date.

5.1.3 Disadvantages of spiral

Can be costly model to use

Doesn’t work well for smaller projects

Figure 29: Diagram of Spiral



5.1.4 When to use spiral

For medium to high-risk projects

Long-term project commitment unwise because of potential changes to economic

priorities

Users are unsure of their needs

5.2 Waterfall Methodology

(According to the WATERFALL MODEL, 2015) Waterfall model in software engineering

was formally introduced as an idea, through a paper published by Winston Royce in 1970.

The waterfall model is a popular version of the systems development life cycle model for

software engineering. [14] Often considered the classic approach to the systems development

life cycle, the waterfall model describes a development method that is linear and sequential.

[15] Waterfall development has distinct goals for each phase of development.

Figure 30: Structure of Waterfall Methodology



As said earlier the waterfall model has been structured on multiple phases especially to help

out the software construction companies to develop an organized system of construction. [16]

5.2.1 Advantages of Waterfall

(According to the buzzle, 2014)

Simple to implement

The amount of resources required to implement this model are minimal.[17]

After every major stage of software coding, testing is done to check the correct

running of the code. [17]

5.2.2 Disadvantages of Waterfall

You cannot go back a step; if the design phase has gone wrong, things can get very

complicated in the implementation phase.[17]

Often, the client is not very clear of what he exactly wants from the software. Any

changes that he mentions in between may cause a lot of confusion. [17]

Small changes or errors that arise in the completed software may cause a lot of

problems. [17]



5.3 Agile methodology

(Rouse, 2014) “In software application development, agile software development (ASD) is a

methodology for the creative process that anticipates the need for flexibility and applies a

level of pragmatism into the delivery of the finished product. “

(WEBOPEDIA, 2012) The agile methodology, focus on 'agility' and 'adaptability' in

development. Instead of one time-consuming and rigid development schedule, agile models

involve multiple iterative development schedules that seek to improve the output, with the

every iteration. [18]

Each iterations goes through all the steps of design, coding and testing, the design is not set

in stone and is kept open to last minute changes due to iterative implementation.[18]

(According to the My-project-management-expert, 2014)

Figure 31: Agile methodology life cycle

[Source: Buzzle, 2014]



5.3.1 Advantages of Agile

Fast moving, cutting edge developments can be quickly coded and tested using this

method, as a mistake can be easily rectified.[19]

Due to short sprints and constant feedback, it becomes easier to cope with the

changes. [19]

It is easier to deliver a quality product in a scheduled time. [19]

5.3.2 Disadvantages of Agile

If a task is not well defined, estimating project costs and time will not be accurate. [19]

If the team members are not committed, the project will either never complete or fail.

It is good for small, fast moving projects as it works well only with small team. [19]



5.4 Hybrid model

Hybrid is the combination of the models. In this type of models, it adopts suitable required

characteristics and procedure that meet the requirements of new project. Following diagram

shows hybrid model of waterfall and incremental software development.

5.4.1 Why we choose Hybrid Model

The three models that usually used for the vast majority of the projects are, waterfall model,

spiral and hybrid model. “Hybrid Model” which combines the features of the two common

development models: waterfall and spiral.

Figure 32: Hybrid Model Structure

We can use Hybrid Model to all small, medium and large systems and flexibility to start with

any phase either planning ,requirements collection and analysis, designing or programming

representation in accordance to system type and complexity.

(According to Mohammed, 2013)

Build

Test

Operatio

n and

maintenance Implement

ation

Physical design

Logical design

Requirements Analysis

Feasibility study

Initiation

(Author’s work Based on lecture slides-PPSP)



Few advantages of using Hybrid Model

Easy to understand and implement.

Identifies deliverables and milestones.

Works well on mature products and weak teams.

Good for management control (plan, staff, track)

Works well when quality is more important than cost or schedule

(Based on lecture slides)

Comparison between the hybrid model and spiral model

Hybrid Model Spiral Model

This model works with small, medium and

large projects

Works with large and critical projects

Identifies the point of end for each phase Frequently and overlapping phases

Focus on the planning phase risk

management

focus on only risk management

Table 4: Comparison between the hybrid model and spiral model



5.5 Justification of selection method

By considering my project background, goals and facts, I consider to use hybrid

methodology. As the hybrid methodology combines the approaches of waterfall and spiral

methodologies I feel that it is possible to give rich documentation about the research.

Actually face recognition is the very wide subject that needs to have good research to build

such a robust system. So by using waterfall method I can have a best research on the related

subjects within short period of time.

The waterfall methodology is normally suitable for development of programs that are already

stable and their design doesn’t need a major makeover. But even the “Agile “models are

applicable in every area of software development, it’s best suited for web based applications.

Since this waterfall is a sequential, that is suite for this project.

When considering the Spiral model, it’s incorporates with the iterative nature of software

development and incorporate with the all the advantages of both the model and prototyping

model but main disadvantage in spiral is need to have good knowledge and requires a

expertise in risk analysis and sometimes it looks like so complicated model to understand in

some scenarios.

Moreover, allocated developing duration is short and busy with our assignments and exams

this project requires a rapid developing approach in short period of time. So as Hybrid

methodology is combined with aspects of two or more models, somehow I managed to do my

best on my project.

Anyhow choosing a right and suitable life-cycle methodology can be difficult in some cases.

Actually it depends on the how you think. But for good approach it might essential to have

one. Life cycle methodology covers not only just development, it must be appropriate for

product support and maintenance



CHAPTER 6

6 TECHNICAL RESEARCH AND INVESTIGATION

Face recognition has been object of much interest in the last years. It has many applications

in a variety of fields such as identification for law enforcement, authentication for banking

and security system access, and personal identification among others. There are different

developing platforms for face recognition. But selecting a suitable platform is very important

thing. It has briefly analyzed developing platforms and APIs, in this chapter.

6.1 Developing platforms

6.1.1 Dot.net

.NET is a programming framework created by Microsoft that developers can use to create

applications more easily. (Microsoft, 2014) The .Net Framework is a technology that support

building and running the next generation applications and XML services [20]. Dot net

platforms enrich with different powerful object oriented languages, functional programing

languages, virtual machine environment, and code libraries.

The .Net Frameworks Architecture

(Based on Dot.Net Corporate web site )

Figure 33: Dot Net Frameworks



VB.Net, C#.Net, C++ are consider dynamic Dot.Net languages, WPF (Windows Persistence

Language), and Silverlight can consider as cross platforms that helps to increase functionality

of the programs. [22]

Microsoft .NET Framework is a software framework which is installed in Microsoft

Windows. The software which are developed using .NET require .NET Framework to be

installed in your system to be able to run. [22]

Once you install .NET Framework, it creates the required software environment so that it can

provide the appropriate runtime requirements to the software.

.NET Framework can be installed in Windows NT, 98, Me, 2000, XP, Vista, Windows 7,

Windows 8 and Windows Server 2003, 2008 and 2012.

Dot.Net framework 3.0 and the latest version that release for windows 8 windows server

2012 can consider as most reliable and stable Dot.Net frameworks which is available now for

download.



6.1.2 .Net Languages

VB.Net, C#, Visual C++ are the most popular dynamic languages in Dot.Net framework and

in computer science industry. J#, F#, Iron Python also can consider as Dot.Net Languages.

6.1.2.1 Visual Basic.Net

(Robinson ,2001) Visual Basic is an Object Oriented Language, which inherit from Visual

Basic 6 and VB .net support lot of OOP concepts. VB.Net was created especially for VB

developers and has a number of unique features and functions that make a great choice for

building. [18]

In VB.NET, the functionality in that base class is available to your other classes as is, or you

can extend and modify it as necessary [18].So VB.NET has some advantages over the other

.NET languages.

6.1.2.2 Visual C++.Net

Visual C++ is a computer language that is used to create applications. C++ has its origin in its

parent language named C [19]. Actually, both C and C++ are mostly used as a foundation for

computer programming and they are available on various types of computers. [19]

Visual C++ is the Microsoft implementation of C++ language which is totally support OOP

concepts and this support by huge number of APIs.



6.1.2.3 Visual C#. Net

C# .net which is popular programming language similar to VB.Net but C sharp has more object

oriented capability than VB.Net. And support huge variety of APIs and It will immediately be

familiar to C and C++ programmers. C# combines the high productivity of Rapid Application

Development (RAD) languages and the raw power of C++ [21].

Many programming environments are available. Probably the most popular programming

environment you can use to write C# instructions is Microsoft Visual Studio [20].

6.2 Sun Java

Sun Java is a software platform, which build on java virtual machine. Sun Java developed by

Microsoft system. In modern world java, consider as powerful developing platform. One of

the reason for popularity of the Java is Java is free platform because of that most developers

adapted Java and developed Java platforms.

6.3 Java architecture

Java architecture provides a portable, robust, high performing environment for development.

Java provides portability by compiling the byte codes for the Java Virtual Machine, which is

then interpreted on each platform by the run-time environment. Java is a dynamic system,

able to load code when needed from a machine in the same room or across the planet.



6.4 Compilation of Code

When you compile the code, the Java compiler creates machine code (called byte code) for a

hypothetical machine called Java Virtual Machine (JVM). The JVM is supposed to execute

the byte code. The JVM is created for overcoming the issue of portability. The code is written

and compiled for one machine and interpreted on all machines. This machine is called Java

Virtual Machine.

6.4.1 Characteristics of Java

(According to the Vogella, 2015)

Platform independent: Java programs use the Java virtual machine as abstraction and do not

access the operating system directly [23].

Object Oriented Language: Except the primitive data types, all elements in Java are objects

[23].

Strong Typed Programing Language

Automatic memory management: The program does not have direct access to the memory

[23].



6.5 Image processing APIs

APIs are intermediate software which have designed to communicate with software for

exchange and data process them.

An API consists of set of libraries which gives functionality to perform process and

communication in between software.

(Roos, 2014) An API is a software-to-software interface, not a user interface. With APIs,

applications talk to each other without any user knowledge or intervention [23].

An image processing field widely using APIs are Aforget.net, OpenCv, and EmguCV.

6.5.1 OpenCv

(According to the OpenCv, 2014) OpenCv (Open Source Computer Vision Library) is an

open source computer vision and machine learning software library [24]. OpenCv was built to

provide a common infrastructure for computer vision applications and to accelerate the use of

machine perception in the commercial products [24].

OpenCv has over 2500 optimized algorithms for image processing.

6.5.2 EmguCV

(According to the EmguCV, 2014) Emgu CV is a cross platform .Net wrapper to the OpenCv

image processing library [25]. Allowing OpenCv functions to be called from .NET compatible

languages such as C#, VB, VC++, Iron Python etc. The wrapper can be compiled by Visual

Studio [25].



One of the main disadvantage of OpenCv is it not easy to integrate it with Dot.Net

environment so as a solution for that OpenCv wrapper developed which support to OpenCv

and Dot.Net integration.

6.5.3 Aforge.Net

(According to the Aforget.Net, 2014) AForge.NET is an open source C# framework

designed for developers and researchers in the fields of Computer Vision and Artificial

Intelligence - image processing, neural networks, genetic algorithms, fuzzy logic, machine

learning, robotics, etc. [26].

This is one of the drawback of this framework.

6.6 Developing approaches

During research, following developing possibilities has identified.

6.6.1 Approach one

Extracted face region using EmguCV

In this approach as mentioned above EmguCV integrated with Visual Studio and developing

language like c# to exchange data. EmguCV doesn’t provide face recognition ability using

Eigenfaces method.



6.6.2 Approach two

As mentioned in previous topics implemented Haar-features for detect objects over the

project scope and duration, there for this approach should be consider again when implement.

6.6.3 Approach three

In this approach uses EmguCV for face detection so Emgu supports Haar based face region

detection.

6.7 Justification of selection of programming environment

When considering all the above potential environments, it’s all the programing environments

provide similar features and techniques with minor differences.

Dot.Net and developing platform and java platforms are competitive platforms which gives

similar features and performance. When comparing with windows application development

Dot.Net environment is more compatible with Microsoft technologies because Microsoft

developed both Dot.Net and windows.

Dot.Net development environment, Visual studio provides really advanced and completed

IDE for implement where Java development requires third party development tool for java

development. Dot.Net debugging is very effortless because of that it can fix the bugs quicker

and much easier.

.Net provides and IDE which helps developer to do development easily while java users

would need to use 3rd party tools for development. Main difference between java and .Net

framework is .Net framework lacks platform independence.



When considering API support Dot.Net supported by lot of image processing APIs

comparing with java environment. By considering above fact .Dot Net has selected as

developing platform and visual studio 2008 selected as developing software.

As mentioned before C++ shows higher efferent rate and as mentioned before C++ has lot

support from API including OpenCv, EmguCV and Afforge.net. Therefor C++ gives lot of

support to image processing.

Visual C# and Visual basic.net are simple language than C++. In addition both of them have

less capability to implement image processing. However it doesn’t mean that by using those

languages, it’s not possible to implement image processing applications.

But when considering overall, visual studio C# is easy to coding and managing. C# can

consider as one of the most efficient language. As mentioned in above C# has both

capabilities of C++ and java. Because of that C# can identify as flexible and advance

language which can adopt to develop this project.



CHAPTER 7

7 SYSTEM DESIGN

This chapter provides logic design of the system by using UML diagrams, Use Case Diagrams,

class diagram and activity diagram have used during the representation of logic design of the

system. This design has based on analysing and concluding facts on during research phase.

During analysis, the focus is on what needs to be done, independent of how it is done. During

design, decisions are made about how the problem will be solved, first at high level, then at

increasingly detailed levels.

System design is the first design stage in which the basic approach to solving the problem is

selected. During system design, the overall structure and style are decided. The system

architecture is the overall process of the system into components called subsystems. The

architecture provides the context in which more detailed decisions are made in later design

stages. By making high level decisions that apply to the entire system, the system designer

partitions the problem into subsystems so that further work can be done by several designers

working independently on different subsystems.



7.1 Use case diagrams

This diagram has designed to understand functional requirements and users of the system.

There are six use cases which extend from the two main use cases. The following use case

diagram shows overall view of functionalities which are expected from the system.

A Use-case analysis. Its purpose is to present a graphical overview of the functionality provided

by a system in terms of actors, their goals and any dependencies between those use cases.

The main purpose of a use case diagram is to show what system functions are performed for

which actor. Roles of the actors in the system can be depicted.

The use case diagram shows the position or context of the use case among other use cases. As

an organizing mechanism, a set of consistent, coherent use cases promotes a useful picture of

system behavior, a common understanding between the Admin, user and the criminal and the

development team.



SystemAdmin

New User

Add Record

View Record

Add Training images

Find the match

Login

FaceRecognition

Figure 34 : Use Case Diagram



7.1.1 Use case diagram for admin

Admin

Face Recognition

Current UserNew User

Add New User

Detect the face

Face Extraction

Normalizing

Face Detection

<<include>>

<<include>>

<<include>>

<<include>>

<<extend>>

Face RecognitionAuthentication

FaceDetection

Normalization

Face Extraction

<<extend>>

<<include>>

<<include>>

<<include>>

Figure 35: Use Case Diagram for admin



7.1.2 Use case diagram for Admin and Criminal

Figure 36 : Use Case for Admin and Criminal



7.2 Use case descriptions

7.2.1 Add new record

Use case

Add new record

Description

- It had identify that adding new user to

database is a critical activity. This allows

adding new records to database. And

process them to store in the database

Actor

- User

Assumptions

- Operator has log in to the system

Main flow

- Actions

- Fill the relevant forms and upload the

images in to the database

- Verify the submitted images

- Detect the Face region

- Extract the face region

- Store the captured details in to the

database

- Display “Successfully Saves Your Data”

Alternative flow

-Actions

- The input image does not contains any

faces

- Prompt the error message

Alternative flows

- The input images contains faces to detect

but it is occluded that is not possible to

detect faces or face regions

- error message display

Table 5: Use Case Description -Add New User



7.2.2 View record

Use case

Add new record

Description

- View Record

Actor

- User

Assumptions

- If record is available it will show the

record

Main flow

- Actions

- Ask to show /browse the records

- Select record to view

- Show the record

Alternative flow

-Actions

- If record is not available

- Show “not available message”

Table 6 : Use Case Description For View Record



7.2.3 Train image space

Use case

Add new record

Description

- It is necessary to create a face space. This

feature allows user to create face space and

store it in the database for future use.

Actor

- User

Assumptions

- There are enough images that need to

create face space

Main flow

- Actions

- User enter no of Eigen Vectors need to

create

- User create image space type

- System create image space

- Store it in the database

- Display successful message

Alternative flow

-Actions

- If database is not accessible

- Display “Database Error “ message

Table 7: Use Case Description of Train Image Space



7.2.4 Find the matched image

Use case

Recognise Face

Description

- To compare face regions of stored records

to find

Actor

- Operator

Assumptions

- This will provide accurate results if there

are relevant face/ face regions

Main flow

- Actions

- Submit the face region

- display the face region

- Identify submitted face region

- Verify the identify face region

- Match or compare with the training images

which stored in the database.

-Display the particular criminal details after

recognitions process done.

Alternative flow

- Actions

- If submitted images is not in the database

- provide an error message that the detected

face is not save or store in the database.

Alternative flow

- Action

- If submitted images not a face region

- provide an error message

Table 8 :Use Case Description -Find The Matched Image



7.3 System Overview

Following diagram illustrate relationship between components of the system. These

relationship should be identify and understand properly before starting implementation of the

system.

Table 9 : System Architecture



7.4 E-R Diagrams (Process flow)

7.4.1 E-R diagram between administrator and the operator

7.4.2 E-R diagram between operator and the criminal

Figure 38: 7.4.2 E-R diagram between operator and the criminal

Figure 37: E-R diagram between administrator and the operator



7.5 Activity Diagrams

(Tutorials Point, 2014) Activity diagram is another important diagram in UML to describe

dynamic aspects of the system [27].

Activity diagram is basically a flow chart to represent the flow form one activity to another

activity. The activity can be described as an operation of the system [27]. It means basically

what are the steps that system use to archive it is goal. Following diagrams illustrate

functionalities of the system.



7.5.1 Administrator Process

Figure 39: Activity Diagram For System Administrator



7.5.1.1 Add records

This is the flow of adding faces to the system. First it checks weather inserted image is a face

image or a non-face image, if it is a face image it checks weather face regions of the face is

extractable. Else if submitted the face image does not contain any face images it prompts a

message and ends the process. If it is possible to extract face region from the face image, it

extracted the detected face from the image and save it in the database with the info of the

criminal.



Figure 40: Activity Diagram For Add Records



7.5.2 Operator Process

Figure 41 : Activity Diagram for Operator Process



7.6 Sequence diagram

(Visual-Paradigm, 2015) The sequence diagram models the collaboration objects based on

time sequence. It shows how the objects extract with others in particular scenario of a use

case [28].

7.6.1 Administrator

Figure 42: Sequence Diagram for Administrator



7.6.2 System Operator

Figure 43 :Sequence Diagram for operator



7.7 Collaboration diagrams

(Smart-draw, 2015) A collaboration diagram describes interactions among objects in terms of

sequenced messages [29].

7.7.1 Administrator

Figure 44: Collaborative diagram for Administrator



7.7.2 Operator

Figure 45 : Collaboration diagram for operator



7.8 Component Diagram

(Smart Draw, 2015) A component diagram describes the organization of the physical

components in a system [30].

Figure 46: Component Diagram



7.9 Deployment Diagram

Figure 47: Deployment Diagram



7.10 Class Diagram

Figure 48: Class Diagram



CHAPTER 8

8 TESTING AND EVALUATION

The system testing is performed after the implementation to ensure that the system is

functioning according to the specification without errors and performance of the system is

satisfactory. Testing allows measuring the system, evaluating the system and to find out the

bugs of the system. Then quality of the system can improve.

As the system implementation was carried out in four increments, at the end of the each

increment, it was possible to do the testing on developed modules, at the end of each

increment. Once all the modules were completed and integrated the overall system testing

was done.

8.1 Test scope

The testing phase involves the testing of development system using various data. Preparation

of the test data plays a vital role in system testing. After preparing the test data, the system

under study was tested using those data. While testing the system, by using the test data,

errors were found and corrected by using the following testing steps and corrections were

also noted for future use. Thus, a series of testing is performed on the proposed system before

the system is ready for implementation. The system has been tested in the following areas.



8.1.1 Unit testing

Unit testing is carried out at each increment to determine that the each module is functioning

as required. Therefor errors could be identified and rectified prior to the integration of

modules within the system.

Following core unit has identify to be tested

Pre-processing module

- Image normalization unit

Face region extraction module

- Face detection

- Face region extraction

Verification module

- Face and face features verification unit

8.1.2 Integration testing

Once a module developing during an increment was tested, it was integrated with other

modules of the system.

It will check following module for integration testing

Face region extraction

Matching module

Verification module

8.1.3 Overall system testing

At the end of all four increment of system implementation and unit testing and integration

testing, overall system testing was done to ensure that the final system was functioning

without errors and according to the specification.



8.2 Testing Strategy: Unit Testing

Each module of the system was unit tested and at the end of increment. The following entrance and exit

criteria are identified. . All independent paths were exercised to ensure that all statements in the module

have been executed at least once and all error-handling paths were tested.

Each unit is thoroughly tested to check if it might fail in any possible situation. This testing is

carried during the programming state itself. At the end of this testing phase each module is

found to be have an adverse effect working satisfactorily, as regard to the expected output

from the module.

Entrance criteria: the unit to be tested must be coded

Exit criteria: the unit should be logically correct and also bugs identified should have been

correct

8.2.1 Test case 01- Video Preview of main window

Test Type Unit test

Test Unit Video Preview of main window

Test Plan Connect webcam to the PC and run the

program

Success Criteria Connect should be established to the web

cam and input video should be displayed in

the main window.

Input Video from a webcam

Output(successful) Display video in main window

Output(unsuccessful) - if connection to the web cam cannot be

established

- display error message

Table 10: Test case 01: Video Preview Of Main Window



8.3 Testing Strategy: Integration Testing

Integration testing was carried out at the end of each increment to determine that the

integrated modules were functioning according to the specification without errors are

required.

Data can be lost across an interface, one module can on another; sub-functions when

combined may not produce the desired major function. Global data structures can present

problems. Integration testing is a systematic technique for the program structure while at the

same time concluding tests to uncover errors associated with interface. All modules are

combined in this testing step. Then the entire program is tested as a whole. Each of the

module is integrated and tested separately and later all modules are tested together for some

time to ensure the system as a whole works well without any errors.

Entrance Criteria: All the units developed in an increment are integrated.

Exit Criteria: The integrated units perform in the expected behaviour without errors.



8.3.1 Test Case: 01 Face detection and face extraction

Test type Face detection and face extraction

Test Unit/functionality Detect the face region

Description In this case system check whether the input

image is a face or not. If it is a face the

region of the face will be detected.

Test plan - browse the image from the disk

- Click on detection button to detect the

faces

- After that detection of all the faces will be

extracted from the image

-if it is a group image, all the faces will be

extracted and by clicking on next and

previous button can view all the extracted

faces.

Input An image that the format of JPEG,PNG

Output (Successful) Display message of verification success and

the number of images that detected

Output (Unsuccessful) Display an error message

Table 11: Test Case: 01 Face detection and face extraction



8.4 Face detection and counting unit

This section consists of test cases which was help to test human face detection and counting

faces.

Test case 01 Test Name: Count human faces

Why is this test performed

This test case ensure that, to count human

faces , if there is one there

Input

Image (group of faces)

Accepted output

Successfully count and gives at least 2 faces

out of 4 faces

Actual output Successfully system counts the faces

- Given faces : 4

- Successfully count :2

- Unsuccessful count :2

Table 12 : Test case 01: Count Human Faces



8.5 Testing Strategy: Overall System Testing

Overall system testing was done after integration testing ensure that the modules integrated

within system increments were functioning as specified as a final product.

8.5.1 Test case: Overall System Testing

Test Type System Integrated Testing

Rational

This is ensure that overall system is

functioning according to the specification

and without errors but have some

limitations.

Test plan

Adding a new record

Description

This checks whether the input image is an

image or not. If it is an image it detect the

image features and save it in the database

along with the criminal ID

Test plan -Click on start recognition button in main

window button

-Check relevant out put

-Check database values to verify

Success criteria

- Display extracted face region

Output (Unsuccessful)

- Display an error message

Number of entered records

50

No of successful detections

41

Table 13: Test case: Overall System Testing

Test Summary



8.5.2 Conclusion of overall system testing

The overall system testing was completed with the majority of success. In here the face

detection module, face detection and face extraction were tested as functional requirements.

Integration test case design methods can also make use of random and partition tests. In

addition, scenario based testing and the tests derived from behavioural models can be used to

test a class and its collaborators. A test sequence tracks the flow of operations across class

collaborations.

OO system validation testing is black box oriented and can be accomplished by

applying the same black box methods known for conventional software. However scenario

based testing dominates the validation of OO systems, making the use case a primarily driver

for validation testing.

We mainly concentrated on scenario based testing strategy. Some of the test cases for

scenario based testing are given below.



CHAPTER 9

9 IMPLEMENTATION

This chapter is mainly focused on methods that were used to develop the proposed system.

Since the proposed system requires computer vision to perform the face detection aspects of

the project a computer vision library was also used in the implementation to help the

development process.

9.1 Implementation face detection

Face detection is one of the main component of this system. It is as important as all the other

components.

First of all need to insert an image from the computer to picture box and it filter the weather

the image is in correct format like *.jpg;*.bmp;* or .gif.. Then image is turned in to grey-

scale, as well as Bitmap image and sent it to cascade object so that the image can be template

matched to see if there is any face in the image. This is done using the code given below.

Figure 49: Code for Image Loading



After it send it to the cascade object to detect the faces.

Figure 50 : Code for cascade object

The result will be stored in an array and checked if there are any face area found. If face area

is found that area will be selected and copy of that area will be extracted. The code for storing

in array is given below

Figure 51: Save detected faces in an array



Output will be looks like this:

Figure 52: Final Result of face detection

9.2 Implementation face extraction

Once faces have been detected from an input image, extract each single face out and view on

the windows form in a picture box like below:

Figure 53: Extracted Faces



9.3 Implementing Training Set Manager

- New user interface control to make set of training images

In here operator can save criminal images with the crop of the criminal face in the XML file.

So the operator can match with training images with the help of training images.

In here the user can detect the faces by giving image to the system. Sometimes system detect

faces in a wrong way in this cases the user can select the face manually.



CHAPTER 10

10 CRITICAL EVALUATION

A critical evaluation was performed on the completion of this project and it was determined

that project has met the functionality expected of it as mentioned at the initiation of the

project. Further this section provides a review of the project objectives and problems

encountered and solutions adapted in achieving objectives and knowledge and experience

gained through the process of this project.

During the primary research performed and studied information about face detection and face

recognition and also face features extraction techniques , based on that technical research was

carried out research about the developing platforms and developing strategies. The secondary

research was done at the next phase based on requirements identification analysis. In

secondary research current issues and challenges in creating successful face recognition

system is identified and then few of widely used solutions are discussed and compared.

This project is basically focused on identify persons using their face region and face features.

It has identified importance of using face regions for recognition during numerous reasons.

At the end of the research it analysed all selected details and finally conducted to use Viola

Jones Haar-Features Cascade classifier as face region detection techniques.

Identifying humans faces with their images have been successfully implemented in certain

areas. It can be applied to surveillance systems and access control systems under various

environments. Accuracy of the computer based face recognition systems have been criticized

as failure rates of such systems is quite high comparing to the other biometric identification

systems.

After the research was done then it was analysis part. The main functionalities of the system

were identified and the requirements specification made. The requirements were divided in to

two parts which are Functional and non-functional requirements. The initial system was

described by using activity diagrams and sequence diagrams.



The next section was the implementation. The system was implemented using Visual Studio

C# and other libraries and API’s that support windows operating system. The OpenCv

computer vision library is used to implement the face detection. The library is used and it is

wrapped to C# using Emgu CV wrapper.

While the developing the system the proposed system there were many obstacles. Even

though Open CV was used as the computer vision library, the library was not compatible with

Visual Studio C#. This was a major setback when implementing the system.

The critical appraisal was performed after the system was implemented. The developed

system detect most of human faces and extract the faces from the image with the high

accuracy rates. As the conclusion of critical evaluation it can be mentioned that objectives of

the project set the initiations has be achieved satisfactory and knowledge in face recognition

techniques and skills in C# programming were greatly improved.



10.1 Limitations

The following limitations have been identified when considering the overall functionality of

the system

Detecting non faces

- Some times it detect faces and non faces also. And in feature extraction part also it extract

faces and the non faces too.

Make ups and Masks

- Make ups and Masks are the another challenge in face recognition, so hope to develop this

system to identify human faces even he or she wear a Masks or sunglasses whatever.

Improve face detection methods used

- Still the face detection method can only detect the faces up to a certain angle.



10.2 Suggestions for Future Enhancements

The following future enhancement are proposed in order to overall limitations of

functionality identified above and improved the application in general.

a) The Future enhancements of this project include the following:

The criminal photos may be of any size.

By selecting any one cropped part of the criminal, we can get the full image of the

criminals along with details from the google image and the other related images.

New face constructed by different cropped parts can be saved.

b) Use an algorithm to measure facial expressions and angle

A robust algorithm like Neural Network should be used to improve the system’s

accuracy in above conditions.

c) Improve as a security application

As the current system is a generic face recognition application, it can be improved as

security application which we can be applied to a wide variety of application areas

including access control for PCs, airport surveillance, private surveillance, criminal

identification and for security in ATM transactions. In addition, face recognition system

is moving towards the next-generation smart environment where computers are

designed to interact more like humans.



CHAPTER 11

11 Conclusion

On conclusion of this project it can be mentioned that the project has effectively achieved the

objectives defined at the initiation of the project. The scope of the project is to implement a

criminal identification system which it can compare the witness images with the training

images. It can be stated that the scope of the project is feasible since the requirements of the

project has been successfully met.

For the successful implementation of the system, a wide research had to be done with the

busy time table of our institute. Even though I had a good dream to implement this system,

because of lack of time I was unable to implement my system in a successfully way. But the

process of the system I have clearly documented.

It’s not only academic achievements gained, while carrying out this project relatively the

project process helped in developing many personal skills such as time and resource

management. However through the process of this project, a better understanding of

techniques and also many more API’s available in the computer science vision industry.



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14 APPENDICS

14.1 Gantt chart



14.2 Testing results

14.2.1 Test case 1: Video Preview and face detection

14.2.2 System training

System records 10 faces as training images

Save the 10 training faces in a XML file



14.2.3 Image based face detection

14.2.4 Detect many faces and extract the faces from an image



14.2.5 Manual face detection



14.3 End Results:

14.3.1 Test case 01: Face Detection

Input image Detected area Detected faces Result

1 success

1 success

0 fail

1 success



0 fail

1 success

1 success

0 fail



1 success

1 success

Times tested 10

Successful results 7

Failed results 3

Percentage of success 70%

Percentage of failure 30%



14.3.2 Test case 2: Face Verification

Input Image Threshold value Face verified Result

4757.342 True success




- False Fail



14.3.3 Test cases 3: Face matching

Test 01:

Input image

Matching results

Time duration: 30 seconds

No of testing during 30 seconds: 5 times

Successfully matched with 5 training images

Result: success



Test 02:

Input image

Matching results:




Result: success



Test 03:

Input image

Matching results:




Result: success



Test 04:

Input image

Matching results:



Not matched with any training images

Result: success, because system is not train with that face

Display message:



Test 05:

Input image

Matching results:




Result: success



Test 06:

Input image

Matching results:



Not matched with any training images

Result: success, because system is not train with that face

Display message:



Test 06:

Input image

Matching results:




Result: success



15 User Manual

User have to log in to the system by giving correct user name and password. If the user

provide correct username and password the user can log in to the system. And the

confirmation message also pop up and inform the user that the user has successfully log in to

the system. If not error message display.

The user can see the Home page like below. And the Operator can select the option by

clicking on navigation buttons.



In here system get 10 training images via a web cam. After that user can save these images in

a xml file as shown below

Jayashan-cb004082-Criminal Face Recognition-Final

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