An Efficient Approach to Extract Singular Points for Fingerprint Recognition

Supervised By: Dr. Muhammad Sheikh Sadi

Associate Professor, Department of Computer Science and Engineering, Khulna University of Engineering & Technology

Contact: sheikhsadi@gmail.com

Submitted By: MD. Mesbah Uddin Khan

Level-4, Term-2, Department of Computer Science and Engineering, Khulna University of Engineering & Technology

Contact: mesbahuk@gmail.com

Dated: June 10, 2012

Problem Statement

• Over the years many approaches have been proposed for developing fingerprint recognition systems. But some of them give inaccurate results due to low-quality images or have high time cost. We will focus on singular points extraction from low quality image and then matching fingerprint within low time cost.

Things we need to know

• Fingerprints • Singular Points • Fingerprint Recognition

Fingerprint (1/2)

•The fingerprint is a duplicate of a fingertip epidermis. •When a person touches a smooth surface, the fingertip epidermis characteristic transferred to the surface. •The pattern of the ridges and valleys on the human fingertips forms the fingerprint images.

Fingerprint (2/2)

• Fingerprints have remained a valuable

means of identification of an individual because:

1. they are totally unique to the individual

2. they never change (Immutability)

Fingerprint Ridge

Ridge patterns

All fingerprints divided into 3 classes ▫ Loops ▫ Whorls ▫ Arches

Fingerprint Features

Two types of features 1. Local Features ▫ Ridge Ending ▫ Bifurcation

2. Global Features ▫ Core ▫ Delta

Singular Points

a special pattern of ridge and valleys formed by global features like core and delta are called singularities or Singular Points (SP).

▫ A core is defined as the top most

point on the inner most ridge

▫ A delta defined as the center point where three different directions flows meet.

State of the Art

• Poincaré Index is the most commonly used method for locating the singular points

• Merits ▫ easy to understand and implement

• Demerits ▫ it may lead to false detection in noisy images

State of the Art

• Intersection-Based Method • proposed by Ramo et al., singular points are

taken as the intersections of transition lines

• Demerits • As they are intersected, these intersect

points may not give accurate result always. • This method needs high quality images

State of the Art

• Singular candidate method • uses both the local and global features • introducing singular candidate models that

indicate the positions where the probability of the existence of singular points is high

• Demerits • sometimes gives false candidate region • noisy images may also be extracted to find

singular points

Proposed Methodology

• The proposed methodology is composed of two main phases: 1. Singular point Extraction

2. Fingerprint Recognition.

Proposed Methodology

• The proposed methodology is composed of two main phases: 1. Singular point Extraction

a) Image Filtering b) Directional Image Extraction and detecting

DF’s angle c) Extracting Singular points

2. Fingerprint Recognition.

Proposed Methodology

• The proposed methodology is composed of two main phases: 1. Singular point Extraction

a) Image Filtering b) Directional Image Extraction and detecting

DF’s angle c) Extracting Singular points

2. Fingerprint Recognition. Relative distance, variance and standard

deviation calculation for multiple singular points

Image Filtering

Step 1: Take an input image of defined WIDTH and HEIGHT.

Step 2: For all the pixels in the image Do the following.

a. Calculate each pixel’s RGB values

b. If R=O, G=O & B=O Then

Put a BLACK pixel i.e RGB(0,0,0) to the pixel.

Else

Put a WHITE pixel i.e RGB(255,255,255) to that pixel.

Loop.

Directional Image Extraction

•

Marr-Hilderth Filter

•

Gaussian Filter

•

Extracting Singular Points 1/2

• For the extraction of singular points from the directional image, the following pseudo code is applied.

Extracting Singular Points 2/2

Core and Delta Region

Fingerprint Recognition

Schema:

Calculations

We know, • Variance,

• Standard Deviation,

Where, x denotes the distances Using these relations we can calculate relative

distance, variance and standard deviation for multiple singular points

Experimental Setup

A short list of tools and libraries used for this experimental setup are given below: • OS : Microsoft Windows 7 Prof. Edition • IDE : Microsoft Visual Studio 2010 • Framework: .NET Framework 4.0 • Language : C# • Library : AForge.Imaging • Database : FVC 2004 (DB4)

System

Experimental Results 1/2

The proposed method is applied on 75 fingerprint images selected from FCV 2004 database. • It detects 65 true and 4 false core points out of 70. ▫ accuracy rate 92.85%

• It detects 36 true and 3 false delta points out of 39. ▫ accuracy rate 92.31%

Singular Points Total Missed False

Core 70 5 4

Delta 39 3 3

Experimental Results 2/2

The proposed approach is applied in FCV2004(DB4) fingerprint image database for recognizing fingerprints. • It recognized and matched fingerprints in of 24

runs out of 26 runs.

• The overall accuracy rate of fingerprint recognition is found 92.31%

Comparisons 1/4

• Comparison of missed singular points extraction rate between different methods:

Methods Missed Core (%) Missed Delta (%)

Chikkerur 4.74 76.7

Peng 10.04 30.0

Yin 4.65 14.1

Proposed 7.14 7.69

Comparisons 2/4

• Figure: Missed Core and Delta rate comparison

0

10

20

30

40

50

60

70

80

90

Chikkerur Peng Yin Proposed

Missed Core (%)

Missed Delta (%)

Comparisons 3/4

• Comparison of false singular points extraction rate between different methods:

Methods False Core (%) False Delta (%)

Chikkerur 22.54 0.0

Peng 12.14 6.67

Yin 5.42 6.4

Proposed 5.71 7.69

Comparisons 4/4

• Figure: False Core and Delta rate comparison

0

5

10

15

20

25

chikkerur peng yin proposed

False Core

False Delta

Concluding Remarks

• This thesis work proposes and implements a technique for detecting fingerprints using Singular Points for both high and low resolution image. It also helps Recognizing them in low cost and less time overhead.

• This thesis works with FCV 2004 database images,

which were much noisy. For better results Fingerprint Scanners can be used.

Future plan

• This thesis work supposes that all the fingerprint images are in straight orientation. So while a fingerprint is rotated than sometimes it fails to recognize it.

• All of these works can undergo further study for better results

Thank you all…