Palmprint Identification using FRIT

Authors:Dakshina Ranjan Kisku, Ajita Rattani, Phalguni

Gupta, *C. Jinshong Hwang, Jamuna Kanta Sing

Presented By –Prof. C. Jinshong Hwang

Department of Computer Science, Texas State University, San Marcos, Texas 78666, U.S.A

25 - 29 April 2011Orlando World Center Marriott Resort & Convention CenterOrlando, Florida, USA

Outline of Talk:

� Biometrics� Palmprint Biometrics

� Hand Geometry� Palm Characteristics

� Advantages of Palmprint Trait� Proposed Palmprint Identification System� ROI Extraction� Feature Extraction using FRIT� Classification with Bayesian Classifier� Experimental Results

� CASIA Database� IIT Kanpur Database� Results� Comparative Study

Biometrics:

Biometrics authentication is a method by which one can be recognized based on one or more intrinsic physical or behavioralhuman characteristics.

Palmprint Biometrics:

� Hand Geometry� Features: Hand shape and dimensions, finger size and lengths

� Palm Characteristics [2], [16]� Features: Principal lines, wrinkles and creases

� Principal lines: Heart line, head line and life line� Wrinkles: Weaker and irregular lines, much thinner than principal

lines

� Creases: More like fingerprint structure, have ridges and valleys

Advantages of Palmprint System:

� Advantages [2], [16]:� High distinctiveness� High permanence� High performance� Non – intrusiveness� Low resolution imaging� User – friendly� Low price palmprint devices and low setup cost� Highly stable

Proposed Palmprint Identification System:

� ROI [25] is detected and extracted from palm image.

� FRIT [24], [26] is applied on the ROI (region of interest) to extract a set of distinctive features from palmprint image.

� These features are used to classify with the help of Bayesian classifier [30].

� The proposed system has been tested on CASIA [16], [28] and IIT Kanpur [31] palmprint databases.

Preprocessing: ROI Extraction

To extract the ROI of palm image the following steps are followed:

� Convert the palm image to a binary image. Gaussian smoothing is used to enhance the image.

� Apply boundary-tracking algorithm in [25] to obtain the boundaries of the gaps between the fingers. Since the ring and the middle fingers are not useful for processing. Therefore, boundary of the gap between these two fingers is not extracted.

Contd…..ROI Extraction

� Determine palmprint coordinate system by computing the tangent of the two gaps with any two points on these gaps. The Y-axis is considered as the line which joining these two points. To determine the origin of the coordinate system, midpoint of these two points are taken through which a line is passing and the line is perpendicular to the Y-axis.

� Finally, extract ROI for feature extraction which is the central part of the palmprint.

Feature Extraction using FRIT:

� To characterize palmprint image Finite Ridgelet Transform [24], [26] is used to achieve a very compact representation of linear singularities.

� FRIT captures the singularities along lines and edges.

� As the continuous Ridgelet transform has close relations with other transforms in continuous domain, the continuous Ridgelet Transform for a given palmprint can be defined as

where in 2-D are defined from a wavelet function in 1-D as

Contd……

Therefore, the separable continuous wavelet transform of I(x, y) in space can be written as,

2ℜ

Contd…..� The wavelets are functions with scale and line position and wavelets

are found to be effective in representing point singularities.

� Ridgelets are found to be effective in representing singularities along the line.

� Ridgelets can be considered as concatenation of 1-D wavelets along the line.

� In particular, wavelets and Ridgelet transforms are related to Radon transform.

� Radon transform is a projection of image intensity along a radial line oriented at a specific angle.

� It extracts lines in edge dominated images while palmprint images are considered to be an edge dominated images.

Contd….

Therefore, for a given integrable bivariate function I(x, y), the Radontransform (RDT) is defined by

Continuous Ridgelet transform (CRT) is considered as the application of1-D wavelet to the slices of Radon transform. Therefore,

Contd…..

Contd…..

Each element in feature vector in Equation (11) can be obtained by

where imax and jmax represent the total number of points in horizontaland vertical directions in the FRIT frame, respectively.

Classification using Bayesian Classifier:

� In pattern recognition and classification field, Bayesian classifier [30] is found to be one of the best classifiers to classify the objects.

� Bayes error is the best criterion to evaluate feature sets.

� A posteriori probability functions are optimal features.

� Let denote the object classes and Ppalm is an palmprint image in feature space.

� Therefore, the posteriori probability function of xi given Ppalm is defined as

nxxxx ,...,,, 321

Contd…..

� The palm image Ppalm is classified to xi of which the posterioriprobability given Ppalm is the largest among all classes.

The within class densities are usually modeled as normal distributions

where is a priori probability, the conditional probabilitydensity function of xi and is the mixture density.

The Maximum A Posteriori decision rule for the Bayes classifier is

)( ixP )|( ipalm xPp

)( palmPp

Contd….

where Mi and Σi are the mean and covariance matrix of class xi,respectively. From Equation (13) the decision function can be written as

Therefore, a palmprint feature vector Ppalm is assigned to class xj if

Experimental Results:

� CASIA Database� 5502 palmprint images / 312 subjects� Left and right palms� 8-bit gray scale JPEG images� Taken with uniform-colored background� Uniform distributed illumination� Normalized to 140×140 pixels

� IIT Kanpur Database� 800 palmprint images / 400 subjects� Resolution is set to 200 dpi� Images are rotated by at most ±35 degree

� Images are normalized to 140×140 pixels

Contd….

Table 1. Recognition Rates in CASIA Palmprint Database

Table 2. Recognition Rates in IIT Kanpur Palmprint Database

Contd…..

Table 3. Comparative Study in terms of Error Rates

Conclusion:

� An efficient palmprint identification system has been presented in this paper where Finite Ridgelet Transform (FRIT) and Bayesian classifier are used.

� Shortcomings of wavelets are handled by the Finite Ridgelet Transforms and they extend the functionality of wavelets to higher singularities.

� Palmprints are classified using Bayesian classifier.� All intra-class and inter-class comparisons are made for

determining the recognition rates.� The three rank based recognition rates are presented.� The experimental results reveal better performance of the

proposed system when it is compared with other existing systems.

References:

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Contd…..

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Questions ???

Thank you !Thank you !Thank you !Thank you !

Contact Author:

drkisku@ieee.org