palm print recognition using local binary patt

7/29/2019 Palm Print Recognition Using Local Binary Patt

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Palm Print RecognitionUsing Local Binary Patternand Support Vector Machine


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1.INTRODUCTION

The Key reasons for a Biometric PersonalIdentification

Traditional Methods may allowunauthorized access.

A password assigned to each user maybe guessed by an unauthorized user or

forgotten by the authentic user.

1/13/2011 INTRODUCTION


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1/13/2011 INTRODUCTION

Fig. 1 System Block diagram


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1/13/2011 INPUT IMAGE

Image from PolyU database(2nd version)


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1.IMAGE SEGMENTATION

1.Algorithm to find points between the fingers2.Obtaining the Region of Interest3.Image Pre-processing

1/13/2011 IMAGE SEGMENTATION


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1/13/2011 IMAGE SEGMANTATION

II. A.

Using a low pass Gaussian filter with a particularthreshold (say T), the palm print image isconverted from gray scale image to a binary

image.


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Fig. 2 Key Point Detectionbetween 2 fingers.

The set of 3 horizontal pixels just

above the point of interest shouldbe at the binary value one. The set of three horizontal pixels

just below the point of interestshould be at the binary value of

zero. The pixels on the left and right of

the points should have zero level. The pixels on the left and right of

the points of interest at aparticular distance should havezero level.



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Fig. 3 (a) Palm Image before rotation (b) Palm Image just beforecropping the ROI

II. B.


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Fig. 4 (a) Original Image (b) Square segmented ROI

Using L2 as shown in Fig. 3(b) a square regionis obtained as shown in Fig. 4(b).


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Image Pre-Processing

In order to increase the computational efficiency,the ROI is compressed using the waveletdecomposition.

Furthermore, in order to enhance the main

features in the palm such as the principle lines,ridges and wrinkles, a Laplacian filter is appliedto the ROI to sharpen the features of the palm,followed by histogram equalization to increase its

contrast and finally a Gaussian filter is applied tothe ROI to smoothen out the edges.


II. C.


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1.FEATURE EXTRACTION

1.Sobel Operator2.Local Binary Pattern3.Feature Representation

1/13/2011 FEATURE EXTRACTION


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Sobel Operator isused to find theedges of an image in

a specified direction.The ROI is operatedon in eight directionsby the Sobeloperator


Fig. 5 Examples of directional responses

derived using Sobel operator. (a) Originalpalm print images, (b) to (e) components ofthe images in the horizontal, vertical, positive45, and negative 45 directions.

III. A.


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1/13/2011FEATURE EXTRACTION

The Sobel masks used are given in Fig. 6.

1 2 1 0 1 2 -1 0 1 -2 -1 0

0 0 0 -1 0 1 -2 0 2 -1 0 1

1 2 1 -2 -1 0 -1 0 1 0 1 2

Fig. 6 3x3 Sobel Operators in different directions

-1 -2 -1 0 -1 -2 1 0 -1 2 1 0

0 0 0 1 0 -1 2 0 -2 1 0 -1

1 2 1 2 1 0 1 0 -1 0 -1 -2


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Local Binary Pattern


III. B.

The operator assigns a label to every pixel in animage by thresholding the eight neighborhoodpixels by the gray value of its centre. The featurevector is formed by concatenating thethresholded binary bits in the anti-clockwisedirection, which is shown in Fig. 7.

An LBP string is called uniform if it consists of atmost 2 bit-wise transitions from 0 to 1 or vice-versa.

This way the operation results in a set of 59 LBPpatterns denoted by {1, 2 59}.


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Fig. 7 Obtaining a Binary label for a pixel


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Feature Representation


III. C.

A histogram of labels can be constructed as:

The palm print images are divided into 9 equallocal regions and the texture descriptor is extracted

from each region independently. Application of the Sobel operator in four directions

yields four Sobel images which are divided into 9sub-windows each being a 40x40-pixel image.

Thus, the texture descriptor for a given palm printwill have a size of {59 (no. of labels) x 9 (no. ofsub-windows) x 8 (direction components)} asshown in fig. 8.


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MATCHING

Chi-Square Method Support vector Machine

1/13/2011 MATCHING


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Chi-Square Matching

Where Sb and Mb are normalized enhanced

histograms to be compared; indices b refers to the bthbin of the histogram, which in this case varies from 1to 59.

1/13/2011 MATCHING

The Chi-Square distance between the twohistograms S and M can be defined as


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1/13/2011


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Support Vector Machine

SVM looks for Hyper Plane Maximizes Margins Minimizes quantity proportional to number

of misclassification errors Creates decision boundary defined by:

1/13/2011 MATCHING


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A few kernel function that have been used inthis study are Radial basis function : Sigmoid kernel :

Polynomial kernel function: Linear kernel:

1/13/2011 MATCHING


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The experiments were performed on 2different sets of Databaseo PolyU_Palmprint_Database (2nd Version): It

contains 40 palm images of , 193 differentuserso IIT Delhi Database : This database was built at

Biometrics laboratory, IIT Delhi and contains a

minimum of 7 palm-images of 109 users

1/13/2011 IMPLEMENTATION &RESULTS

IMPLEMENTATION


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1/13/2011 IMPLEMENTATION &RESULTS

Kernel Function used withSVM

PolyU Database IIT-D Database

Linear 0.50 1.00

Polynomial 1.00 1.60

Database Chi-SquareMatching(EER %)

GAR whenFAR=1%

GAR whenFAR=2%

PolyU 2.11 95.72 97.72

IIT-D 3.51 92.23 94.45

Table 1: Error Rate plot using SVM as matching technique and different kernelfunctions.

Table 2: Results using Chi-square matching technique

RESULTS


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1/13/2011

Thank You

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palm print recognition using local binary patt

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