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The International Journal of Computer Science and Information Security (IJCSIS Vol. 9 No. 2) is a reputable venue for publishing novel ideas, state-of-the-art research results and fundamental advances in all aspects of computer science and information & communication security. IJCSIS is a peer reviewed international journal with a key objective to provide the academic and industrial community a medium for presenting original research and applications related to Computer Science and Information Security. . The core vision of IJCSIS is to disseminate new knowledge and technology for the benefit of everyone ranging from the academic and professional research communities to industry practitioners in a range of topics in computer science & engineering in general and information & communication security, mobile & wireless networking, and wireless communication systems. It also provides a venue for high-calibre researchers, PhD students and professionals to submit on-going research and developments in these areas. . IJCSIS invites authors to submit their original and unpublished work that communicates current research on information assurance and security regarding both the theoretical and methodological aspects, as well as various applications in solving real world information security problems.
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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 2, February 2011
PIFS CODES BASED FOR
BIOMETRIC PALMPRINT VERIFICATION
I Ketut Gede Darma Putra
Departement of Electrical Engineering, Faculty of Engineering
Udayana University, Bukit Jimbaran, Bali - Indonesia
email : duglaire@yahoo.com
Abstract — This paper proposes a new technique to extract resolution images can be used, low cost capture devices
the palmprint features based on some fractal codes. The can be used, it is very difficult or impossible to fake
palmprint features representation is formed based on position palmprints, and their characteristics are stable and unique
of range blocks and direction between the position of range [18].
and domain blocks of fractal codes. Each palmprint
Recently, many verification/identification technologies
representation is divided into a set n blocks and the mean
value of each block are used to form the feature vector. The using palmprint biometrics have been developed
normalized correlation metrics are used to measure the [2],[3],[4],[5],[11],[12],[13],[18],[21]. Zhang et al. [21]
degree of similarity of two feature vectors of palmprint applied 2-D Gabor filter to obtain the texture features of
images. We collected 1050 palmprint images, 5 samples from palmprints. Pang at al. [13] used the pseudo-orthogonal
each of 210 persons. Experiment results show that our moments to extract the features of palmprint. LI et al. [12]
proposed method can achieve an acceptable accuracy rate transformed the palmprint from spatial to frequency
with FRR = 1.754, and FAR= 0.699. domain using Fourier transform and then computed ring
and sector energy features. Connie at al.[2] extracted the
Keyword; biometrics, fractal codes, fractal dimension, texture feature of palmprint using PCA and ICA. Wu et
feature extraction, palmprint recognition al.[18] extracted line feature vectors (LFV) using the
magnitudes and orientations of the gradient of the points
on palm-lines. Kumar et al.[11] combined the palmprints
I. INTRODUCTION and hand geometries for verification system. Each
The personal verification becomes an important and palmprint was divided into overlapping blocks and the
highly demanded technique for security access systems in standard deviation value of each block was used to form
this information area. Traditional automatic personal the feature vector.
recognition can be divided into two categories: token- In this paper, we propose a new technique to extract the
based, such as a physical key, an ID card, and a passport, features of palmprint based on fractal codes. This
and knowledge-based, such as a password and a PIN. technique is different with the method in [4] and [5].
However these approaches have some limitations. In the
token-based approach, the “token” can be easily stolen or
lost. In the knowledge-based approach, the “knowledge” II. IMAGE ACQUISITION
can be guessed or forgotten [21]. In order to reduce the All of palm images are captured using Sony DSC P72
security problem caused by traditional methods, biometric digital camera with resolution of 640 x 480 pixels. Each
verification techniques have been intensively studied and persons was requested to put his/her left hand palm down
developed to improve reliability of personal verification. on with a black background. There are some pegs on the
Biometric-based approach use human physiological or board to control the hand oriented, translation, and
behavioral features to identify a person. The most widely stretching. A sample of the hand and pegs position on the
used biometric features are of the fingerprints and the most black board is shown on Figure 1 (a).
reliable are of the irises. However, it is very difficult to
extract small minutiae features from unclear fingerprints
and the iris input devices are very expensive [19]. Other
III. PALMPRINT EXTRACTION AND
biometric features such as of face, voice, hand geometries,
NORMALIZATION
and handwritten are less accurate. Faces and voices can be
mimicked easily, hand geometries and handwritten can be This paper used new technique to extract the ROI
faked easily. (region of interest) of palmprint. This technique consists of
Palmprint is the relatively new in physiological two steps in center of mass (centroid) method. These steps
biometrics [18]. There are many unique features in a can be explained as follow.
palmprint image that can be used for personal recognition. a. The gray level hand image is thresholded to obtain the
Principal lines, wrinkles, ridges, minutiae points, singular binary hand image. The threshold value was computed
points and texture are regarded as useful features for automatically using the Otsu method. To avoid the
palmprint representations [21]. A palmprint has several white pixels (not pixel object) outside of the hand
advantages compared to other available features: low- object is used median filter.
47 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 2, February 2011
b. Each of the acquired hand images needs to be aligned
in a preferred direction so as to capture the same
features for matching. The moment orientation method
is applied to the binary image to estimate the
orientation of the hand. In the method, the angle of
rotation ( θ ) is the difference between normal axis and
major axis of ellipse that can be computed as follows.
1 2 µ1,1 (a) (b) (c)
θ = tan −1
(1)
2 µ 2,0 − µ0,2
µ p ,q = ∑∑ (m − m ) (n − n )q
p
(2)
m n (d) (e) (f) (g)
where µ p,q th
represent the (p,q) moment central, and Figure 1. Extraction of palmprint, (a) original image, (b)
( m, n ) represents center of area is defined as binary image of (a), (c) object bounded, (d) and (e)
position of the first centroid mass in segmented binary and
1 1
m= ∑∑ m , n = N ∑∑ n ,
N m n
(3) gray level image, respectively, (f) and (g) position of the
m n second centroid mass in segmented binary and gray level
where N represents number of pixel object. image, respectively.
Furthermore, the grayscale and the binary image are
rotated about ( θ ) degree.
c. Bounding box operation is applied to the rotated
binary image to get the smallest rectangle which IV. FEATURES EXTRACTION
contains the binary hand image. The original hand
image, binarized image, and the bounded image There are three main steps to extract the palmprint
shown in Figure 1 (a), (b), and (c), respectively. features based on fractal codes proposed in this paper.
d. The centroid of bounded image is computed using These steps can be explained as follows.
equation (3) and based on this centroid, the bounded
binary and original images are segmented with 200 x A. Extraction of fractal codes of palmprint images
200 pixels. The segmented image and its centroid Fractal codes of palmprint images are obtained using
position are shown in Figure 1 (d) and (e). the partitioned iterated function system (PIFS) method. In
e. The centroid of the segmented binary image is PIFS method, each image is partitioned into its range
computed and based on this centroid the ROI of blocks and domain blocks. The size of the domain blocks
grayscale palmprint image can be cropped with size is usually larger than the size of the range blocks. The
128 x 128 pixels. The first and the second positions of relation between a pair of range block (Ri) and domain
centroid in binary and gray level image are shown in block (Di) is noted as
Figure 1 (f) and (g).
Ri = wi (Di ) (6)
This method is so simple. This method has been tested
for 1050 palmprint images acquired from 210 persons, and wi is contracted mapping that describes the similarity
the results show this method is reliable. relation between Ri and Di, and is usually defined as an
Before the feature extraction phase, the extracted ROI affine transformation as below:
are normalized using normalization method in [11] to xi a i bi 0 xi ei
reduce the possible imperfections in the image due to non-
uniform illumination. The method is as below: wi y i = ci
di 0 yi + f i
(7)
zi 0
0 s i z i oi
φ d + λ if I ( x, y ) > φ
I ' ( x, y ) = (4)
φ d − λ otherwise where xi and yi represent top-left coordinate of the Ri , and
zi is the brightness value of its block. Matrix elements ai,
bi, ci, and di, are the parameters of spatial rotations and
ρ d {I ( x, y ) − φ}2 flips of Di, si is the contrast scaling and oi is the luminance
λ= (5)
ρ offset. Vector elements ei and fi are offset value of space.
In this paper, we used the size of domain region twice the
where I and I’ represents original grayscale palmprint range size, so the values of ai, bi, ci, and di are 0.5. The
image and the normalized image respectively, φ and ρ actual fractal code pi below is usually used in practice[19].
(( )( ) )
represents mean and variance of the original image
respectively, while φd and ρd are the desired values for f i = x Di , y Di , x Ri , y Ri , sizei , θ i , s i , oi (8)
mean and variance respectively. This research use φd = 180
and ρd = 180 for all experiments.
48 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 2, February 2011
where (xR , y R ) and (xD , y D ) represent top-left
i i i i
coordinate position of the range block and domain block,
respectively, and size is the size of range block. The fractal
codes of a palmprint image is denoted as follow:
N
F = U fi (9) (a) (b)
i =1
where N represents the number of the fractal code. The
inequality expression below is used to indicate whether the
range and the relevant domain block are similar or not.
d ( R, D ) ≤ ε , (10)
(c) (d)
where d(R,D) represents rmse value, and є is the threshold
Figure 2. Palmprint feature extraction, (a) original image,
(tolerance) value. The range and the relevant domain block
(b) Image I, (c) Image I’, (d) block feature representation
is similar if d(R,D) is less or equal than є. Otherwise, the
block is regarded not similar.
The Figure 2 (d) show the palmprint feature representation
in 16 x 16 sub blocks. Figure 3 shows example of three
B. Palmprint features representation
groups of palmprints from the same palm and palms with
The first step of this method is the forming of angle
similar/different line structures. The features of these
image A as follows.
palmprints are plotted in figure 4. The results show that the
A( j , k ) = α i , j = 1,2,3, K M 1 , k = 1,2,3, K M 2 (11) features of three palm images from the same person are
close to each other than the features of three palm images
yD − yR ,
α i = arctan if j=x and k = y from the different persons with similar or different line
xD − xR i
Ri Ri
structures.
otherwise, α i = 0 (12)
(
where x D , y D
i
)
represent top-left coordinate of the
i V. PALMPRINT FEATURE MATCHING
domain block (see formula (8)) and di represent the angle
between range and domain block. The angle image is not The degree of similarity between two palmprint
binary image representation. The criterion below are added features is computed as follows:
to compute the direction α i . d rs = 1 −
(xr − xr )(x s − x s )T (15)
if xR < xD and yR ≥ yD then αi = αi [(x r − x r )( x r − x r )
T
] [(x
1
2
s − x s )( x s − x s )
T
]
1
2
if xR > xD and yR ≥ yD then αi = 180 − α i where x r , x s are the mean of palmprint feature xr and xs ,
if xR > xD and yR ≤ yD then αi = 180 + α i respectively. The above equation computes one minus
normalized correlation between palmprint feature vector xr
if xR < xD and yR ≤ yD then αi = 360 − α i
and xs. The values of drs are between 0 – 2. The d rs will
if xR = xD and yR ≥ yD then αi = 90
be close to 0 if xr and xs obtained from two image of the
if xR = xD and yR ≤ yD then αi = 270 (13)
same palmprint. Otherwise, the d rs will be far from 0.
Figure 4 shows comparison of feature component of
The criterion sizei = min(size) means the palmprint those palmprint shown in figure 3, and their score are listed
features representation is formed practically using the in Table 1. The matching score of group A are close to 0,
coordinate of the smallest size range block. Later, the and the matching score of group B and C are far from 0.
representation is filtered as follow. The average score of group A, B, and C are 0.1762,
I ' ( x , y ) = I ( x , y ) ∗ h ( x , y )m x n , (14) 0.5057, and 0.6452, respectively. It is easy to distinguish
group A from group B and C using these scores.
h(x,y) is filter which all of its component are one. Figure
2(b) show the palmprint features image of Figure 2(a).
C. Palmprint feature vector
Palmprint feature vector (V) is obtained by dividing
the palmprint image into 16 x 16 blocks, and for each
block its mean value is computed, so obtained the feature
vector V = (v1 , v 2 K , v N ) , where N = 256,and vi is
(a1) (a2) (a3)
Group A: palmprints from the same person
mean value of block i.
49 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 2, February 2011
(b1) (b2) (b3)
Group B: palmprints from different person with similar line
structure
(a)
(c1) (c2) (c3)
Group 3: palmprints from different person with different line
structure
Figure 3. Example of three groups of palmprint
Table 1 Matching Score of groups A, B, and C in figure 3
a1 a2 a3 Average
a1 0 0.1957 0.1404
a2 0.1957 0 0.1925 0,1762 (b)
a3 0.1404 0.1925 0
b1 b2 b3 Average
b1 0 0.5352 0.3056
b2 0.5352 0 0.6763 0,5057
b3 0.3056 0.6763 0
c1 c2 c3 Average
c1 0 0.6900 0.6177
c2 0.6900 0 0.6280 0,6452
c3 0.6177 0.6280 0
(c)
VI. EXPERIMENTS AND RESULTS Figure 4. Comparison of feature component of the
We collected palm image from 210 persons from both palmprint group shown in figure 2. (a),(b),(c) are feature
sexes and different ages, 5 samples from each person, so component of group A, B, and C, respectively. Red, green,
our database contains 1050 images. The resolution of hand blue color are the first, second, and third palmprint in each
image is 640 x 480 pixels. The palmprint images, of size group, respectively.
128 x 128 pixels, were automatically extracted from hand
image as described in the Section 3. The averages of the
first three images from each user were used for training
and the rest were used for testing.
The performances of the verification system are 400
obtained by matching each of testing palmprint images 300
with all of the training palmprint images in the database. A
matching is noted as a correct matching if the two v26 200
palmprint images are from the same palm and as incorrect 100
if otherwise.
0
400
300 250
200
200 150
v24 100
50
100
v22
0 0
Figure 5. Distribution of three feature components
of 1050 palmprints in feature space
50 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 2, February 2011
method for palmprint verification. The experiment results
show that the proposed method can achieve an acceptable
accuracy rate with FRR = 1.7544, and FAR= 06998. In the
future, we will combine the proposed method with wavelet
transformation to extract the feature of palmprint to retain
the block operation.
REFERENCES
[1] Chih-Lung Lin., “Biometric Verification Using
Palmprints and Vein-patterns of Palm-dorsum”,
http://thesis.lib.ncu.edu.tw/etd-db/etd-search/
[2] Connie T., Andrew Teoh, Michael Goh, David Ngo,
2003, “Palmprint Recognition with PCA and ICA”,
sprg.massye.ac.nz/ivcnz/proccedings/ivcnz_41.pdf
[3] C.L. Lin, Biometric Verification Using Palmprints
and Vein-patterns of Palm-dorsum, 2004,
http://thesis.lib.ncu.edu.tw/etd-db/etd-search/
[4] Darma Putra, IKG., Adhi Susanto, A. Harjoko & TS.
Widodo, Palmprint Verification based on Fractal
Codes and Fractal Dimensions, Proceedings of the
Eighth IEASTED International Conference Signal and
Image Processing, Honolulu, Hawai, 2006, 323–328.
[5] Darma Putra, Adhi Susanto, Agus Harjoko, Thomas
Sri Widodo, 2006, Biometrics Palmprint Verification
(a) (b) Using Fractal Method, EECCIS proceedings, Part 2,
pp.22-23, Brawijaya University, Malang, Indonesia.
Figure 6. Performance of verification system,(a) genuine
[6] Duta N., Jain A.K., Mardia K.V.,2002, Matching of
and imposter distribution, (b) FAR/FRR/EER with various
Palmprints, Pattern Recognition Letters, 23, pp. 477-
threshold
485.
[7] Ekinci Murat, Vasif V., Nabiyev, Yusuf Ozturk, 2003,
Table 2. FRR/FAR with various threshold value A Biometric Personal Verification Using Palmprint
Structural Features and Classifications, IJCI
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0.4386 2.0734 0.4734 [8] Jain A.K., 1995, Fundamentals of Digital Image
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0.4586 1.9139 0.5158
[9] Jain A.K., Ross A., and Pankanti S., 1999, A Prototype
0.4626 1.7544 0.6998 Hand Geometry-based Verification System,
0.4746 1.4354 0.9160 www.research.ibm.com/ecvg/publications.html
0.4786 1.2759 1.3552 [10] Jain A.K, Introduction to Biometrics System,
0.4986 1.1164 2.1480 http://biometrics.cse.msu.edu/.
0.5386 1.1164 2.2881 [11] Kumar A., David C.M.Wong, Helen C.Shen, Anil
K.Jain, 2004, “Personal Verification using Palmprint
Figure 6 (a) shows the probability distributions of a and Hand Geometry Biometric”,
genuine and imposter parts with tolerance value = 3, and http:/biometrics.cse.msu.edu/Kumar_AVBPA2003.pdf
feature vector length = 256 (16 x 16 blocks). The genuine [12] LI Wen-xin, David Z,, Shuo-qun XU., 2002,
and imposter parts are estimated from correct and incorrect Palmprint Recognition Based on Fourier Transform,
matching scores, respectively. The result with various Journal of Software, Vol.13, No.5
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rates (FRR) are shown in figure 6 (b). The equal error rate 2003, Palmprint Verification with Moments, Journal of
(EER) of the verification system is 1.2758. Table 2 show WSCG, Vol.12, No.1-3, ISSN 1213-6972, Science
the performance (FAR/FRR) system with some threshold Press.
values. [14] Sarraille, J., 2002, Developing Algorithms For
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is both palmprint feature and palmprint image can be [15] Shu W., Zhang D., 1998, Automated personal
obtained directly from compressed domain (fractal code). identification by palmprint, Opt. eng., Vol. 37, No.8,
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[16] Tao Y., Thomas R.I., Yuan Y.T., Extraction of
VII. CONCLUSIONS AND FUTURE WORK Rotation Invariant Signature Based On Fractal
Geometry, http://cs.tamu.edu
In this paper, we introduced a fractal
characteristics based feature extraction and representation
51 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 2, February 2011
[17] Wohlberg B., Gerhanrd de Jager, 1999, A Review of
the Fractal Image Coding Literature, IEE
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[18] WU Xiang-Quan, Kuan-Quan Wang, David Zhang,
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[19] Yokoyama T., Sugawara K., Watanabe T., Similarity-
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24-26 2006, Oita, Japan,
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[20] Yokoyama T., Watanabe T., Koga H.,Similarity-
Based Retrieval Method for Fractal Coded Images in
the Compressed Data Domain,
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[21] Zhang D., Wai-Kin Kong, Jane You, Michael Wong,
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[22] Zhang D., and W.Shu, Two novel characteritics in
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AUTHOR PROFILE
Dr. I Ketut Gede Darma Putra is a
lecturer in Department of Electrical Engineering and
Information Technology, Udayana University Bali,
Indonesia. He obtained his master and doctorate degree on
informatics engineering from Electrical Engineering,
Gadjah Mada University, Indonesia. His research interest
includes biometrics, image processing, expert system and
Soft computing.
52 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
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