The document presents a palmprint identification system utilizing finite ridgelet transform (FRT) and a Bayesian classifier to enhance recognition performance. It details the advantages of palmprint biometrics, including high distinctiveness and non-intrusiveness, and outlines the methodology for region of interest (ROI) extraction and feature characterization. Experimental results from databases indicate improved classification accuracy compared to existing systems.

1. Palmprint Identification using FRIT
Authors:
Dakshina Ranjan Kisku, Ajita Rattani, Phalguni
Gupta, *C. Jinshong Hwang, Jamuna Kanta Sing
25 - 29 April 2011
Orlando World
Center Marriott Presented By –
Resort & Convention Prof. C. Jinshong Hwang
Center Department of Computer Science, Texas State University,
Orlando, Florida, San Marcos, Texas 78666, U.S.A
USA

2. 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

3. Biometrics:
Biometrics authentication is a method by which one can be
recognized based on one or more intrinsic physical or behavioral
human characteristics.

4. 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

5. 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

6. 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.

7. 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.

8. 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.

9. 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

10. Contd……
Therefore, the separable continuous wavelet transform of I(x, y) in ℜ 2
space can be written as,

11. 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.

12. Contd….
Therefore, for a given integrable bivariate function I(x, y), the Radon
transform (RDT) is defined by
Continuous Ridgelet transform (CRT) is considered as the application of
1-D wavelet to the slices of Radon transform. Therefore,

13. Contd…..

14. Contd…..
Each element in feature vector in Equation (11) can be obtained by
where imax and jmax represent the total number of points in horizontal
and vertical directions in the FRIT frame, respectively.

15. 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 x1 , x 2 , x3 ,..., x n 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

16. Contd…..
where P( xi ) a priori probability, p( Ppalm | xi ) the conditional probability
is
density function of xi and p( Ppalm ) is the mixture density.
The Maximum A Posteriori decision rule for the Bayes classifier is
The palm image Ppalm is classified to xi of which the posteriori
probability given Ppalm is the largest among all classes.
The within class densities are usually modeled as normal distributions

17. 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

18. 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

19. Contd….
Table 1. Recognition Rates in CASIA Palmprint Database
Table 2. Recognition Rates in IIT Kanpur Palmprint Database

20. Contd…..
Table 3. Comparative Study in terms of Error Rates

21. 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.

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25. Questions ???

26. Thank you !
Contact Author:
drkisku@ieee.org