An Efficient Attendance Management Sytem based on...
Transcript of An Efficient Attendance Management Sytem based on...
71 Preeti Mehta, Dr. Pankaj Tomar
International Journal of Engineering Technology Science and Research
IJETSR
www.ijetsr.com
ISSN 2394 – 3386
Volume 3, Issue 5
May 2016
An Efficient Attendance Management Sytem based on Face
Recognition using Matlab and Raspberry Pi 2
Preeti Mehta,
M.tech Student
Mechanical and Automation Department
IGDTUW , New Delhi, India
Dr. Pankaj Tomar,
Assistant Professor
Mechanical and Automation Department
IGDTUW , New Delhi, India
Abstract—A facial recognition system is an application of computer vision which is capable of performing two basic
tasks of identification and verification of person. With the advent of image processing techniques automatic face
recognition is a popular research topic. Conventionally attendance in schools, institutes and universities are taken by
professors and records are stored in registers. This approach waste a lot of time. The objective of this paper is to
instigate a new approach for taking attendance automatically. Local features are extracted by using two techniques
Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) which are used for recognition of faces of
respective students from stored database for attendance marking. Support vector machine (SVM) classifier is used for
comparing database stored features with extracted features from captured images of students.
Keywords—local binary pattern, histogram of oriented gradients, matlab, raspberry pi, support vector machine, viola-
jones algorithm.
1. INTRODUCTION
The face is not merely a set of facial features but it is rather something meaningful. It is an identity of a person
and people adapt to response more to face than other body parts of human. Among the available biometric
techniques face recognition is a popular research topic with number of applications in several industrial areas
including surveillance and security, entertainment and virtual reality and human-machine interactions. With
the advent in image processing technology making computers perform face recognition has become easier.
Maintaining attendance record in educational institutes is an important part of enhancing the quality of
education as attendance points are added at the end of semester. Traditionally attendance is marked manually
by teachers and they must make sure correct attendance is marked for respective student. This whole process
waste a lot of time of lecture duration and due to large strength of students fraudulent and proxy cases are not
intercepted.
Every institute has its own way of taking attendance. Some uses this old manual way for marking attendance
some uses RF Id for marking attendance [1]. Other biometric techniques such as iris, retina, thumb
impression, palm identification, ear recognition [2] is used. But all these methods also waste students’ time as
long queues are formed for marking attendance. Face recognition on the other hand advantage over these
above mentioned techniques as it is nonintrusive, contact-free and has natural acquisition. Universality,
permanence, uniqueness, performance and measurability are the factors which need to be satisfied by a
biometric identifier [3]. Many ideas have been proposed by researchers for biometric attendance system. G.
Lakshmi et al. developed a system for managing students with Open CV and raspberry pi module that is
interfaced with fingerprint device [1]. Aziza Ahemdi et al. [4] proposed an automatic attendance structure
using techniques such as Adaboost for face detection and local binary pattern and histogram of orientation for
features extraction. In paper [5], embedded attendance management system on linux is presented which skin
classification and histogram normalization techniques. Similarly in paper by Naveed Khan Balcoh et al. image
enhancement and skin classification techniques have been used. Abhishek Jha in paper [7] describes the
working of automated attendance system using different color based techniques for face detection and
Principal Component Analysis (PCA) for face recognition. In paper [8] also PCA and Eigen face algorithm is
used for recognition of face. In paper by Prabhhjot Singh, PCA technique in Matlab has been implemented
successfully [9]. There are plenty of face recognition algorithm are available and Chaoyang et al. in [10] has
72 Preeti Mehta, Dr. Pankaj Tomar
International Journal of Engineering Technology Science and Research
IJETSR
www.ijetsr.com
ISSN 2394 – 3386
Volume 3, Issue 5
May 2016
shown the collation of three face recognition algorithms namely PCA, Linear Discriminant Analysis (LDA)
and Elastic Bunch Graph Matching (EBGM) implemented with Matlab. Paper [11], [12] uses the idea of
continuous observation and integral validation process which enhance the reliability and performance of the
system. There are many papers available for automatic attendance management system that uses PCA
algorithm for face recognitions including papers [13] and [14] which implement the program on Open CV and
light tool kit.
In this paper, our purpose is to obtain an automatic attendance system implemented with raspberry pi camera
module and Matlab R2014a version. This paper is divided into following sections as section I includes the
introduction and literature survey, section II includes the system description, section III
goes with the used algorithm explanation, section IV with the practical implementation with section V and VI
ending with the results and future scopes.
2. SYSTEM DESCRIPTION
2.1. Hardware
The system includes raspberry pi 2 model B module with raspberry pi camera module for capturing the
classroom image/ video. Raspberry pi 2 is a credit card sized minicomputer. It has 900 MHz quad-core ARM
Cortex-A7 CPU, ram of 1 GB, 4 USB ports out of which one port is connected for Wi-Fi connector, 40 GPIO
pins for interfacing external devices, CSI (camera serial interface) port for camera connection. Raspberry pi
have its own camera which is light in weight and small in size. It has a still resolution of 5 MP. It can operate
in both image capturing and video mode with the resolution of 1080p30, 720p60, 640p480. The properties of
camera such as region of interest, frame rate, mode of operation etc. can be changed in Matlab when setting
up connection. Figure (1), shows the raspberry pi 2 with camera module.
Figure 1: Raspberry Pi 2 and Camera Module
2.2 Software
The Matlab R2014a is used for implemented the algorithms Viola-Jones, Local Binary Pattern and histogram
of Oriented Gradient. Matlab support package for raspberry pi hardware is installed for building up a
communication link between Matlab and microcontroller. This will help in acquiring data from sensors and
imaging devices connected to the controller board and process them in Matlab. The connection between the
hardware and software can be wired or wireless as per our need.
73 Preeti Mehta, Dr. Pankaj Tomar
International Journal of Engineering Technology Science and Research
IJETSR
www.ijetsr.com
ISSN 2394 – 3386
Volume 3, Issue 5
May 2016
2.3. Flow Chart
3. ALGORITHMS
This section describes the algorithms used for the system. The system is divided into four sections as:
Face detection by Viola-Jones algorithm.
Pre-processing of image.
Features extractions using LBP and HOG.
Face Recognition using Support Vector Machine (SVM).
3.1. Viola – jones Algorithm for Face Detection
The image processing is mainly divided into two tasks, detection and recognition. Detection is defined as
identification of faces and/or extraction of faces from an image. Recognition on the other hand is retrieval of
similar images from a give database of face images. Viola-Jones technique is a most popular technique used
for detecting objects but mainly used for face detection. Rectangular features selection with classified training
and feature selection using Adaboost technique is used. In the end degenerative tree of classifier is formed.
Four main steps are used in this algorithm. Haar feature selection, creating integral image, Adaboost training
followed by cascade classifiers in the end [15].
Haar features are similar to that of kernel convolutions. There are total five types of haar features used for
face detection. The Each feature results in single value which is calculated by subtracting the sum of pixels
under white region from the sum of pixels under black region. Figure (2), show the commonly used haar
features.
Figure 2: Haar Features
Integral image is to reduce the computational time. In this the value at any pixel cell is the addition of
pixels above and to the left of it. Then the sum of all pixels in a rectangle can be calculated easily form the
four values at the corner rectangle of integral image.
Camera
Face Detection
Pre- Processing
Features Extraction
Face Recognition
Attendance Marking
Database
74 Preeti Mehta, Dr. Pankaj Tomar
International Journal of Engineering Technology Science and Research
IJETSR
www.ijetsr.com
ISSN 2394 – 3386
Volume 3, Issue 5
May 2016
Adaboost also know as adaptive boost is used for relevant features selection from the 160,000+
features calculated from 24x24 window size. Adaboost contruct a strong classifier from a linear combination
of weighted weak classifiers and is a machine learning algorithm given by eq. (1)
F(x) = a1f1(x) + a2f2(x) + a3f3(x) + ……. (1)
Where,
F(x) is strong classifier, f1(x), f2(x) and so on are weak classifiers, a1, a2 and so on are weighted values and x is
an image.
Cascade classifier is used in the end of Viola Jones Algorithm. In this stages are formed which
composes of strong feature classifier formed by Adaboost technique. These stages are used to identify whether
the sub window is a face or non face. It forms a degenerated tree as shown in figure (3).
Figure 3: Cascade Classifier
3.2. Local Binary Pattern
It is a most popular feature descriptor used for face recognition and texture classification. LBP value each
pixel value into binary digits i.e. either 0 or 1. Mathematically, it can be explained as follow [16]:
Given a central pixel valued Pc in an image, its neighboring pixels (P0, P1, P2,… Pi-1) is selected by using
radial filter. The response at Pc is calculated by:
LBP = 𝑠(𝑃𝑖 − 𝑃𝑐)𝑖−1𝑖=0 2𝑖 (2)
Where,
s(x) = { 1, x ≥ 0
0, x < 0
The coordinates for each sampling iteration is calculated by circular coordinate system with center Pc and
coordinates given by (Rcos (2πi / P), (Rsin (2πi/P)) where P is the total number of involved neighboring pixels
and R is the radius around centered pixel. mixed units, clearly state the units for each quantity that you use in
an equation.
Value of D pixel
is given by,
D+A – (B+C) D
C
A B
1 2 3
All sub windows
Processing
Rejected sub windows
F
F F
75 Preeti Mehta, Dr. Pankaj Tomar
International Journal of Engineering Technology Science and Research
IJETSR
www.ijetsr.com
ISSN 2394 – 3386
Volume 3, Issue 5
May 2016
Histogram is then built to represent the features by using the above calculated LBP value as :
H (k) = 𝑓(𝐿𝑃𝐵𝑃,𝑅 𝑖, 𝑗 ,𝑘)𝑗𝑗=0
𝑖𝑖=0 (3)
Where k € [0, K]
F(x, y) = {1, x = y
0, otherwise.
K is the maximum LBP value.
3.3. Histogram of Orientation Gradients
It is another most popular feature descriptor used for face recognition [17]. The functioning of this algorithm
is simple. First it computes the gradient of a grey image which is obtained by calculating the x and y direction
derivatives using convolution functions as:
Ix = I × Dx (4)
Iy = I × Dy
Where Dx and Dy are kernels as [-1 0 1] and [1 0 -1] T
respectively.
Then the magnitude, M and orientation angle, Ɵ is calculated:
M = √ Ix2 +Iy
2 and Ɵ = tan
-1 Iy/Ix (5)
The orientation returns a value in between [-180°, 180°]. The next step is to calculate the cell histograms
which is computed for 9 bins varying from [0°, 180°] interval for cell size of 8x8 pixels which will be used for
block descriptors.
The cells are grouped into blocks and normalization of histograms is computed and arranged into 1-D
array.
4. IMPLEMENTATION
An attendance management system is proposed here using Matlab R2014a and Raspberry pi 2 using computer
vision toolbox as:
Formation of students’ database.
Capturing of class room video.
Frame selection from the video.
Face detection by Viola-Jones algorithm
Features extraction by LBP and HOG algorithms.
Face recognition by comparing with database stored features.
Marking of attendance in database.
Following are the screenshots of the implemented project.
4.1. Video Acquisition
The video is record with Raspberry Camera module connected in class room such that the region of interest
captures all the student faces. The camera is connected to the controller board and will transmit the video via
wireless connection using wifi dongle to the Matlab for processing.
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76 Preeti Mehta, Dr. Pankaj Tomar
International Journal of Engineering Technology Science and Research
IJETSR
www.ijetsr.com
ISSN 2394 – 3386
Volume 3, Issue 5
May 2016
Figure 4: Group Photo of Students
4.2. Image conversion
A frame will be selected from the video. The selected image will be in RGB format that will be converted into
grey-scale image for further processing.
Figure 5: RGB to Grey Conversion
4.3. Face Detection
The faces will be detected by Viola-Jones Algorithm. The Matlab provides an inbuilt system object from
Computer Vision Toolbox vision.CascadeObjectDetector for face detection application [15].
Figure 6: Detected Faces
4.4. Pre- Processing of Image
The features will be extracted from the cropped detected faces after pre-processing of images. The pre-
processing step will include the contrast improvement using function imadjust, background removal using
strel function, and converting the grey-scale image into black and white image with removal of small objects
from binary image with bwareaopen function
77 Preeti Mehta, Dr. Pankaj Tomar
International Journal of Engineering Technology Science and Research
IJETSR
www.ijetsr.com
ISSN 2394 – 3386
Volume 3, Issue 5
May 2016
Figure 7: Pre-Processing of Cropped Faces
4.5. Features Extraction
The features will be extracted from the processed image. Total six features will be extracted, three features
nose, mouth and eyes each with two algorithms LBP and HOG and will be stored in the database for face
recognition.
Figure 8: (a) Mouth, (b) Nose, (c) Eyes Feature Extraction
4.6. Face Recognition
The features extracted from the image will be compared with the stored features in database with the help of
SVM. If the system recognizes the features, corresponding name with the features stored will be marked will
present attendance in the database of students detailed.
5. CONCLUSIONS
Despite the low accuracy of face recognition as compared to other biometric techniques our automatic
attendance marking system is functioning with accuracy of 92% as out of 12 faces, 11 faces are recognized
successfully and preserve time wastage when attendance is performed manually. The system is user friendly,
easy to use and reliable which provides more security, privacy and well organized data on board. The system
supports multi-model biometric system which other biometric techniques do not support. The scope of
improvement is always there by improving the images quality and increasing the processor speed for real-
time implementation.
78 Preeti Mehta, Dr. Pankaj Tomar
International Journal of Engineering Technology Science and Research
IJETSR
www.ijetsr.com
ISSN 2394 – 3386
Volume 3, Issue 5
May 2016
6. FUTURE SCOPE
The speed of RAM can improve. More features can be extracted or hybrid feature method can be used for face
recognition. Database management languages can be used for online availability of attendance record which
can be accessed by parents and students individually. Mobile application software can be developed for easy
assessment of records. Multi- cameras can be used so that efficiency can be increased.
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