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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.
1/13/2011 IMAGE SEGMENTATION
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1/13/2011 IMAGE SEGMENTATION
Fig. 3 (a) Palm Image before rotation (b) Palm Image just beforecropping the ROI
II. B.
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1/13/2011 IMAGE SEGMENTATION
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.
1/13/2011 IMAGE SEGMENTATION
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
1/13/2011 FEATURE EXTRACTION
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
1/13/2011 FEATURE EXTRACTION
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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1/13/2011 FEATURE EXTRACTION
Fig. 7 Obtaining a Binary label for a pixel
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Feature Representation
1/13/2011 FEATURE EXTRACTION
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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