The document discusses multispectral palm image fusion for biometric authentication using ant colony optimization. It introduces intra-modal fusion of palmprint images from multiple spectra to improve accuracy. The key steps involve detecting the region of interest, fusing the images using wavelet transforms, extracting Gabor features, selecting optimal features using ant colony optimization, and classifying with support vector machines. Experimental results and conclusions are also presented.

1. Multispectral Palm Image Fusion for
Biometric Authentication using Ant
Colony Optimization
D. R. Kisku, P. Gupta, J. K. Sing, C. J. Hwang
Asansol Engineering College, Asansol – 713305, India
Email: drkisku@ieee.org
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2. Outline of the Talk:
Introduction
Intra-modal fusion
Advantages over Uni-biometrics systems
Palmprint features
Advantages of palmprint biometrics
Multi-spectral palm image fusion
Detection of ROI
Wavelet based multi-spectral palm image fusion
Gabor representation of fused image
Feature selection using ant colony optimization
Classification with SVM
Experimental results
Conclusion
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3. Background: Intra-modal Fusion
Intra-modal fusion refers to fusion of multiple instances obtained from
same modality are linearly combined.
Variations of intra-modal fusion:
Matching scores obtained from multiple instances of the same modality are
fused linearly.
Feature level fusion of feature vectors obtained from multiple instances of the
same modality
Sensor level fusion or image fusion of multiple instances of the same modality
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4. Advantages of Intra-modal Fusion:
Combining the evidences obtained in different forms from the same or
different sources using an effective fusion scheme can significantly
improve the overall accuracy of the biometric system.
Intra-modal fusion can address the problem of non-universality which
often occurs in uni-modal system.
Intra-modal systems can provide certain degrees of flexibility.
The availability of multiple sources of information can reduce the
redundancy in uni-modal system.
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5. Palmprint Features:
Wrinkles
wrinkles are thinner than the principal lines and much more irregular
Creases
Creases are detailed textures, like the ridges in a fingerprint, all over the
palmprint. Creases can only be captured using high resolution cameras.
Heart line (a-b)
Head line (c-d)
Life line (e-f)
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6. Advantages of Palmprint Biometrics:
Stability
User friendliness
One may feel comfort to give palmprint images to capturing devices.
Acceptability
Palmprint is suitable for everyone and it is also non-intrusive as it does not
require any personal information of the user. It requires low resolution
capturing devices, not like fingerprint devices.
Uniqueness
Palmprint features do not change over time.
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7. Multi-spectral Palm Image Fusion:
Biometric palm image fusion at low level [1] refers to a process that
fuses multispectral palm images captured by identical or different
biometric sensors.
The fusion performed at low level produces a fused image in the
spatially enhanced form which contains richer, intrinsic and
complementary information.
[1] D. R. Kisku, J. K. Sing, M. Tistarelli, and P. Gupta, “Multisensor biometric evidence fusion for person authentication
using wavelet decomposition and monotonic-decreasing graph,” 7th IEEE International Conference on Advances in
7
Pattern Recognition, pp. 205—208, 2009.

8. Detection of ROI:
Method of ROI (region of interest) detection [2] is employed to reduce
the error caused by translation and rotation.
This process roughly aligns the palmprint and it does not reduce the
effect of palmprint distortion.
The main steps of preprocessing -
original image,
binary image,
boundary tracking,
building a coordinate system,
extracting the central part as a sub-image
preprocessed result.
[2] D. Zhang, W. K. Kong, J. You, and M. Wong, “On-line palmprint identification,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 25, pp. 1041 – 1050, 2003. 8

9. 9

10. Wavelet based Multi-spectral Palm Image
Fusion:
The wavelet transform [3] provides a multi-resolution decomposition of an
image.
In wavelet based palm image fusion, decomposition is done with high
resolution palmprint images.
Decomposition generates a set of low resolution images with wavelet
coefficients for each level where the basis functions are generated from one
single basis function known as the mother wavelet.
The mother wavelet is shifted and scaled to obtain the basis functions.
Then, it replaces a low resolution image with a multispectral (MS) band at
the same spatial resolution level.
Finally, a reverse wavelet transformation is performed to convert the
decomposed and set to the original resolution level.
The operations of a wavelet fusion scheme are outlined in Fig. 2.
[3] T. S. Lee, “Image representation using 2D Gabor wavelets,” IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 18, pp. 959 – 971, 1996. 10

11. Contd…
The input images are decomposed by a discrete wavelet transform and
the wavelet coefficients are selected using a wavelet fusion rule (viz.
‘maximum’ rule) [4] and an inverse discrete wavelet transform is
performed to reconstruct the fused image.
IDWT
DWT
First Palm Image
Fused Image
Fusion of
DWT Decompositions
Second Palm Image
Wavelet Coefficients
[4] D. R. Kisku, J. K. Sing, M. Tistarelli, and P. Gupta, “Multisensor biometric evidence fusion for person authentication
using wavelet decomposition and monotonic-decreasing graph,” 7th IEEE International Conference on Advances in 11
Pattern Recognition, pp. 205—208, 2009.

12. Gabor Representation of Fused Image:
Fundamentally, 2D Gabor filter [5] can be defined as a linear filter whose
impulse response function is the multiplication of harmonic function and
Gaussian function in which Gaussian function is modulated by a complex
sinusoid.
In this regard, the convolution theorem states that the Fourier transform of
a Gabor filter's impulse response is the convolution of the Fourier
transform of the harmonic function and the Fourier transform of the
Gaussian function.
Gabor function is a non-orthogonal wavelet and it can be specified by the
frequency of the sinusoid and the standard deviations of and. The 2D
Gabor wavelet Filter can be defined as
1 (m ) 2 (n) 2
g ( x , y : f , θ ) = exp( − ( 2 + ) cos( 2 π f ( m )))
2 σ x σ 2y
m = x sin θ + y cos θ ;
n = x cos θ − y sin θ ;
[5] T. S. Lee, “Image representation using 2D Gabor wavelets,” IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 18, pp. 959 – 971, 1996. 12

13. Contd…
where –
f is the frequency of the sinusoidal plane wave along the direction from the
x-axis, and specify the Gaussian envelop along x-axis and along y-axis,
respectively.
This can be used to determine the bandwidth of the Gabor filter.
For the sake of experiment, 200 dpi gray scale fused palm image with the
size of 40 × 40 has been used.
Along with this, 40 spatial frequencies are used with, f = π / 2 (i = 1,2,…,5)
i
and θ = kπ / 8, (k = 1,2,...,8) .
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14. Ant Colony Optimization: Concepts
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15. ACO Background:
Ants [6] navigate from nest to food source
Shortest path is discovered via pheromone trails
each ant moves at random
pheromone is deposited on path
ants detect lead ant’s path, inclined to follow
more pheromone on path increases probability of path being followed
[6] M. Dorigo, L. M. Gambardella, M. Birattari, A. Martinoli, R. Poli, and T. Stützle, “Ant colony optimization and swarm
intelligence,” 5th International Workshop ANTS, LNCS 4150, Springer Verlag, 2006. 15

16. ACO Algorithm:
Starting node is selected randomly
Path selected at random
based on amount of “trail” present on possible paths from starting node
higher probability for paths with more “trail”
Ant reaches next node, selects next path
Continues until reaches starting node
Finished “tour” is a solution
A completed tour is analyzed for optimality
“Trail” amount adjusted to favor better solutions
better solutions receive more trail
worse solutions receive less trail
higher probability of ant selecting path that is part of a better-performing tour
New cycle is performed
Repeated until most ants select the same tour on every cycle (convergence
to solution)
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17. Features Selection using Ant Colony
Optimization:
An ant chooses the new feature point according to the following
β
 arg max { Pil H } if q ≤ q 0
 l∈ N i
k il
j = 
S otherwise

A random variable selected according to the following probabilistic rule
β
 P ij H ij
 if j ∈ N k
 ∑ P il H il
β i
S =  l ∈ N ik

0 otherwise

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18. Contd…
Global pheromone update
Pij = (1 − σ ) Pij + σ ∆ p bs
ij
 bs
1/ L

if (i, j) belongs to the best tour
∆pij = 
bs
0 otherwise


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19. Contd…
Local pheromone update
Pij = (1 − γ ) Pij + γ p 0
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20. Classification with SVM:
SVM [7] is known as statistical learning theory which can be used for
classification of test samples with respect to training samples.
SVMs are built based on the principle of structural risk minimization.
The aim is to minimize the upper bound on expected or actual risk which is
defined as
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R (α ) = ∫ z − f ( x , α ) dP ( x , z )
2
where α is a set of parameters which can be used to define a trained machine
z is a class label associated with a training sample x
f(x, α) is a function which can be used to mapping training sample to class labels
P(x, z) is the unknown probability distribution associating a class label with each
training sample.
[7] C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge
Discovery, vol. 2, no. 2, pp. 121–167, 1998. 20

21. Contd…
Let l denote the number of training samples and choose some η such that 0
≤ η ≤ 1.
On expected risks with the probability 1 - η, the following bound holds
h(log( 2l / h) + 1) − log(η / 4)
R (α ) ≤ Remp (α ) +
l
where h is a non-negative integer called Vapnik Charvonenkis (VC) dimension
[14] and is a measure of the complexity of the given decision function.
The term in R.H.S. is known as VC bound.
Risk can be minimized by minimizing the empirical risk as well as VC
dimension.
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22. Contd…
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23. Contd…
To separate a given training sample, an optimal hyperplane is chosen
from a set of hyperplane.
This optimal hyperplane minimizes the VC confidence that provides
best generalization capabilities.
The optimal hyperplane is used to minimize the sum of the distances to
the closest positive and negative training samples.
This sum is known as the margin of the separating hyperplane.
It can be shown that the optimal hyperplane w—x + b = 0 is obtained by
minimizing ||w||2 subject to a set of constraints.
Classifiers
K ( xi , x j ) = x i • x j K ( xi , x j ) = e
− γ || xi − x j || 2
| xT ωk |
d C ( x, ω k ) =
| x || ω k |
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24. Database and Experimental Protocol:
Database:
CASIA [8] Multispectral Palm Images
3600 Palm Image and 100 Subjects
Palm images captured in two different sessions
In each session 3 different sets of images captured
Each set contains 6 images
Between two sets, a certain degree of posture variations is allowed.
Experimental protocol:
Database is divided into 3 disjoints sets
First set (training set) contains 1985 palm images
Second set (evaluation set) contains 966 palm images
Third set (query set) contains 649 palm images
The training set is used to build client models
The evaluation set is used to obtain the client and imposter scores for
verification thresholds
The query set of palm images are used to obtain the verification rates.
[8] Y. Hao, Z. Sun, T. Tan, and C. Ren, “Multi spectral palm image fusion for accurate contact free palmprint
recognition,” 15th International Conference on Image Processing, pp. 281 – 284, 2008. 24

25. Experimental Results:
Table1. Verification Performance determined on the CASIA MultiSpectral Palm Database
CLASSIFIER KERNEL KERNEL EVALUATION SET QUERY SET
FUNCTION PARAMETER
EER TE FA FR TE
NC -------- -------- 6.19% 12.38 4.51% 6.27% 10.78%
%
SVM Linear -------- 5.02% 10.04 3.09% 5.11% 8.2%
%
RBF γ = 0.015 3.97% 7.94% 2.21% 4.04% 6.25%
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26. Comparative Study:
Table 2. Comparison Table
Method Fusion Rule Database Classifier EER (%)
Method – I [9] CT CASIA Hamming distance 0.5
Method – II [9] SIDWT CASIA Hamming distance 0.58
Proposed Method Haar wavelet with CASIA SVM with RBF 3.97
maximum fusion (Evaluation set)
rule
SVM with RBF (Query 3.125
set)
[9] Y. Hao, Z. Sun, T. Tan, and C. Ren, “Multi spectral palm image fusion for accurate contact free palmprint recognition,” 15th
International Conference on Image Processing, pp. 281 – 284, 2008. 26

27. Conclusion:
An efficient palmprint authentication system has presented by fusion of
multispectral palm images.
Multispectral palm images are fused at low level by wavelet transform and
decomposition.
This fused palm image is further represented by Gabor wavelet transform to
capture the minimal intra-class diversity of the same instances and
maximized the inter-class differences between the different subjects in
terms of neighborhood pixel intensity changes.
Gabor palm responses contain high dimensionality features and due to this
high dimensionality, ant colony optimization (ACO) algorithm is applied to
choose a set of distinct features.
Finally, two different classifiers are used, namely, normalized correlation
and SVM with linear and RBF kernels.
To measure the efficacy and robustness of the proposed system, CASIA
multispectral palm database is used.
The results are found to be encouraging.
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28. Questions/Comments, if any !
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