A Review on Palm Print Verification System

Abstract

Palm Print is one of the relatively new physiological biometrics, attracted the researchers due to its stable and unique characteristics. The rich feature information of palm print offers one of the powerful means in personal recognition. Palm print verification System has long been used and it was found that many research activities were carried out. This paper discusses about the number of research works introduced to overcome the difficulties faced in each stage of palm print verification. Our study on palm print recognition focuses on verifying the palm print in four different stages: Palm print acquisition, Preprocessing, Feature extraction and Palm print matching.

Full text

A Review on Palm Print Verification System
K.Krishneswari
1
,
S.Arumugam
2
1
Tamilnadu College of Engineering, Coimbatore, Tamilnadu, India
krishneswari@gmail.com
2
Nandha Educational Institutions Erode, Tamilnadu, India
arumugamdote@yahoo.co.in
Abstract:
Palm Print is one of the relatively new physiological
biometrics, attracted the researchers due to its stable and unique
characteristics. The rich feature information of palm print offers
one of the powerful means in personal recognition. Palm print
verification System has long been used and it was found that
many research activities were carried out. This paper discusses
about the number of research works introduced to overcome the
difficulties faced in each stage of palm print verification. Our
study on palm print recognition focuses on verifying the palm
print in four different stages: Palm print acquisition,
Preprocessing, Feature extraction and Palm print matching.
Keywords: Biometrics, Feature extraction, Palm print
verification, Palm lines, Palm print matching.
1. Introduction
Biometric is the science of measuring human’s
characteristics for the purpose of authenticating or
identifying the identity of an individual based on
specific physiological or behavioral characteristics [1].
Several types of physiological characteristics used in
biometric are appearance of face, hand geometry,
fingerprint, iris and palm print.
The most widely used biometric feature is the
finger print and the most reliable feature is the iris.
However it is very difficult to extract small unique
features such as minutiae from unclear finger prints and
the iris input devices are very expensive. Other
biometric features such as the face and voice are less
accurate and they can be mimicked easily. The
palmprint is a relatively new biometric feature, has
several advantages compared with currently available
features [1]. The seven factors affect the determination
of a biometric identifier in a particular application:
universality, uniqueness, Permanence, collectability,
performance, acceptability and circumvention as shown
in table 1. Palm print recognition has been introduced a
decade ago. It has gradually attracted the attention of
various researchers due to its richness in amount of
features. Palm is the inner surface of the hand between
the wrist and the fingers. The Palm area contains a large
number of features shown in Fig.1 that can be used as
biometric features such as Principal lines, geometry,
wrinkle, delta point, minutiae, datum point features and
texture [2]. The principle lines are also called as flexion
creases. The formation of these lines is related to the
finger movements, tissue structures and the purpose of
skin. Even the palm prints of identical twins are
different [4]. This is because the genetic code in the
DNA gives general instructions on the way skin should
form in a developing fetus but the specific way it forms
is a result of random events position of fetus in the
womb at a particular moment and the exact composition
and density of surroundings amniotic fluid.
Palm print verification employs either high or
low resolution images. Most of the research on palm
print verification uses the low resolution images [3]. The
palm print Verification system consists of four stages:
Palm print image acquisition, Preprocessing, Feature
extraction and matching as shown in Fig.2. The
palmprint image is acquired using a palm print
scanner .Preprocessing has two parts, image alignment
and region of interest selection. Image alignment is done
by referring to the key points. Region of Interest
selection is the cropping of palmprint image from the
hand image .Feature extraction obtains discriminating
features from the preprocessed palmprints .The
matching compares the captured image features with the
stored templates.
Fig. 1 Different Features of Palm.
International Journal of Computer Information Systems and Industrial Management Applications (IJCISIM)
http://www.mirlabs.org/ijcisim
ISSN: 2150-7988 Vol.2 (2010), pp.113-120

Fig. 2 Stages in Palm print Verification
Table 1. Comparison of Biometric traits (High, Medium
& Low are denoted by H, M, L respectively)
The rest of this paper is organized as follows:
Section 2 reviews palm print acquisition devices, Section
3 summarizes the preprocessing algorithms, Section 4
discusses the feature extraction and matching methods,
Section 5 lists various fusion approaches and section 6
offers conclusion.
2. Palm Print Acquisition
Palm print can be captured by widely used CCD
based palmprint scanners, video cameras, Digital cameras
and Digital Scanner. The Fig. 3 Shows a CCD based
palmprint scanner which attracts the most of the
researchers for acquiring the image because the scanner
have pegs for guiding the placement of hands [3], [5].
CCD camera consists of a set of optical components work
together to obtain the data from the palm. However, the
quality of the Palm print image depends highly on the
camera technology used. Zhang et al. designed the
world’s first online palmprint capture devise at Hong
Kong Polytechnic University [2], [50]. Palm print image
is taken either in pegged or peg less environment. The
digital scanner can acquire high resolution hand image but
requires more time to scan which are not suitable for real
time application.
Digital and video cameras can also be used to
collect palmprint images and these images might cause
recognition problem as their quality is low because they
collect image in an uncontrolled environment [21] with
illumination variations and distortions due to hand
movement. Fig 4 shows an image collected by CCD
scanner and digital camera.
3. Preprocessing
Preprocessing is used to correct distortions, align
different palmprints, and to crop the region of interest for
feature extraction. Research on preprocessing commonly
focuses on five steps 1.Binarizing the palm images
2.Boundary tracking 3.Identification of key points
4.Establishing a coordination system and 5.Extracting the
central part.
Fig. 3 CCD Based Scanner
Most of the research uses Otsu’s method for
binarizing the hand image [32]. Otsu’s method calculates
the suitable global threshold value for every hand image.
According to the variances between two classes one of the
classes is the background while the other one is the hand
image. The boundary pixels of the hand image are traced
using boundary tracking algorithm [21]. The key points
between fingers are detected using several different
Feature
extraction
Image
acquisition
Preprocessing
Register
/verify
Database
Accepted/
Rejected
Matching
points
114
A Review on Palm Print Verification System

implementations including tangent [5], Bisector [13], [28]
and Finger based [7], [8].
The tangent based approach considers the edges
of two fingers holes on the binary image which are to be
traced and the common tangent of two fingers holes is
found to be axis X. The middle point of the two tangent
points is defined as the key points for establishing the co-
ordinate system [5].
Bisector based approach concentrates on not
joining the fingers by converting the upper region of the
fingers and the lower part of the image to white. It aims
in determining two centroids of each finger gaps for the
image alignment since only the centre of gravities within
the defined three finger gap region. After locating the
three finger gaps the centre of gravity of the gaps can be
determined. Then the two centroids of each finger gap are
connected to obtain the three lines. The line drawn
through the centroids of each finger gap region intersects
the palm of a key point.
Han and his team propose two approaches to
establish the co-ordinate system, the first approach based
on the middle [8] and the other based on the point, middle
and ring finger [7].
After establishing the co-ordinate systems, the
central part of the palm prints are segmented using three
classes: Square based segmentation, Circle based
segmentation and Elliptical based segmentation. Among
all these methods most of the researchers uses Square
based method because it is easier for handling translation
variation.
Fig. 4 Two palm print collected by a) CCD Scanner
b) Digital camera
Fig. 5 (a) Key Points Identification (b) ROI Extraction
4. Feature Extractions and Matching
After preprocessing of palm print images features
can be extracted for matches. There are two types of
recognition algorithms, verification and identification. In
verification, the system validates a person’s identity by
comparing the captured biometric data with her own
biometric templates stored in the system database.
Verification is typically used for positive recognition,
where the aim is to prevent multiple people from using the
same identity. In identification, the system recognizes an
individual by searching the templates of all the users in the
database for a match. Verification algorithms must be
accurate. Identification algorithms must be accurate and
fast..Research on feature extraction and matching methods
can be classified into 4 categories: Line-based, subspace-
based, Statistical-based and coding based.
4.1 Line Based Approaches
The line-based approach either develop edge
detectors or employ the existing edge detection methods
to extract palm lines [16], [17], [19], [26], [31], [36], [37],
[38], [46], [52]. The palm lines are either matched directly
or represented in other format for matching [41], [42].
Wu et al. use sobel masks to compute the
magnitude of the palm lines [19], [35]. The magnitude are
projected along the x and y directions to form histograms.
They designed two masks to compute the first order
derivative and second order derivative of palm print
images. The first order and second order derivatives can
be obtained by rotating the two masks. The Zero crossing
of the first order derivative is used to identify the edge
points and corresponding directions. Second order
derivative is used to identify the magnitude of the lines.
The weighted some of the local directional magnitude is
regarded as an element in the feature vector. Euclidian
distance is used for matching [34].
Kumar et al. integrated line like feature and
geometrical features for personal verification [53], [39].
115 Krishneswari and Arumugam

Palmprint
Accept\Reject
Accept\Reject Accept\Reject Accept\Reject
Accept\Reject
Fingerprint
Fig. 6 Levels of fusion in a bimodal biometric system;
FU: Fusion Module, MM: Matching Module, DM: Decision Module.
Kung et al. formed a feature vector based on a
low resolution edge map. The feature vector is passed into
decision based neural networks [36].Han et al. used sobel
and morphological operations to extract line like features
from palm print images [7].
Huang et al. proposed a two-level modified
finite radon transform and a dynamic threshold to extract
major wrinkles and principal lines. Two binary edge maps
are compared based on pixel-to-area comparison [46].
4.2 Subspace Based Approaches
Sub space based method is also called
appearance based approach, generally involve principal
component analysis (PCA), Linear discriminant analysis
(LDA) and independent component analysis (ICA).The
subspace coefficient are considered as features .In addition
to applying PCA, LDA and ICA directly to palm print
images, researchers also employ wavelets, Discrete cosine
transform and kernel in their method [6], [9], [10], [11],
[14], [20], [24], [15].
Dale et al. proposed discrete cosine transform
(DCT) based feature vector for palmprint representation
and matching compared with DFT and wavelet transform
[5].
Laadjel et al. approach combines fisher’s linear
discriminant (FLD) and Gabor Wavelet responses. This
method involves convolving a palmprint image with a
series of Gabor wavelets at different scales and rotations
before extracting features from the Gabor filtered image
[4].
4.3 Statistical Approaches
Statistical approaches are categorized into local
and global statistical approaches. Local Statistical
approaches transforms images into another domain and
divide the transform into several small regions. Local
statistics such as means and variances of each small region
are calculated and regarded as features [8], [12], [18], [27],
[29], [40].
Yong et al. method for feature extraction divides
the palm print image into a set of n small regions and then
calculates the mean and S.D of sub regions. Euclidian
square norm is employed for matching [10].
Feature Extraction
Module
Matching
Module
Decision Module
Fingerprint
Tem
p
late
Feature Extraction
Module
Matching
Module
Decision Module
Palmprint
Tem
p
late
FU
MM DM
Template
FU
DM
FU
116
A Review on Palm Print Verification System

Researchers compute global statistical features
like moments, centre of gravity and density directly from
the whole transformed images [9], [32].
4.4 Coding Approaches
Coding approaches encode the filter coefficient
as feature using Gabor filters [43], [47]. Daugman, the
inventor of Iris code, has demonstrated that the bitwise
hamming distance allows real-time brute force
identification in large databases.
Palm code uses a single Gabor filter to extract the
local phase information of palm print [2], [5], [40],[48].
D.Zhang et al. uses the first version of fusion code to
avoid correlation that results from palm code. It involves
the use of four directional Gabor filters to generate four
palm codes and these palm codes are combined [22].
In the second version of fusion code, D.Zhang et
al. identified that the optimal number of Gabor filter is
two. The second version of fusion code is much more
effective than the first.
Palm code and fusion code employ quantized
phases as features and the hamming distances as matches.
The first version of competitive coding scheme uses
multiple two dimensional Gabor filters to extract
orientation information from palm lines. This information
is then stored in a feature vector called the competitive
code. The angular distance is used for comparing two
codes [54].
In second version of competitive code, 25
translated templates are generated from an input palm
print to match the template in a database [4].
Kong et al. introduced a fusion code method to
encode the phase of the filter responses from a bank of
Gabor filters with different orientation. Kong et al.
developed a competitive code method to encode the
orientation information and achieve the state -of –the –art
palm print Verification accuracy [54].
Wu et al. modified fusion code to extract the
orientation field and uses the hamming distance for
matching [35], [43].
Some approaches combine several image
processing methods to extract palmprint features and
employ some standard classifiers such as Neural networks
to make the final decisions [25],[49],[51],[33].
Yue et al. proposed a modified fuzzy c-means
cluster algorithms to determine the orientation of
filters .This achieves higher verification accuracy [26].
Zu et al. use probability feature image (PFI) in
order to suppress random noises in feature image and
fuzzy logic was employed in matching algorithm [6].
Chen et al. [44], [55] perform a two dimensional
dual tree complex transform on the preprocessed a
palmprints to decompose the images. Dual –tree complex
transforms are proposed to resolve the weakness of
traditional wavelet transform which is not shift –invariant,
for pattern recognition. Then they apply Fourier transform
on each sub band and regard the spectrum magnitude as
features. Finally, SVM is used as a classifier.
Hennini-Yeamans et al. employ log –Gabor
filters to assign line content scores to different regions of
palm prints [25]. A Specific number of regions with top
line –content scores are selected to train correlation filters.
They use optimal tradeoff synthetic discriminant function
(OTSDF) filter as a classifier.
5. Fusion
Fusion of multiple traits of an individual can
improve the matching accuracy of a biometric system.
Some of the limitations such as noisy data, intra-class
variations, spoof attacks and unacceptable error rates of a
unibiometric system can be addressed by designing a
system that consolidates multiple sources of biometric
information
.
Multimodal biometric systems are those
which utilize, or are capability of utilizing, more than one
physiological or behavioral characteristic for enrollment,
verification, or identification. The multimodal biometrics
has drawn more and more attention in recent years due to
its promising applications and theoretical challenges [1].
5.1 Levels of fusion
Based on the type of information available in a
certain module, different levels of fusion can be defined as
shown in fig 6.
(
a) Fusion at the data or feature level:
Either the data itself or the feature sets originating from
multiple sensors/sources are fused. (b) Fusion at the
match score level: The scores generated by multiple
classifiers pertaining to different modalities are combined.
(c) Fusion at the decision level: The final output of
multiple classifiers is combined.
Biometric systems that integrate information at
an early stage of processing are believed to be more
effective than those systems which perform integration at
a later stage. Since the feature set contains richer
information about the input biometric data than the
matching score or the output decision of a matcher, fusion
at the feature level is expected to provide better
recognition results. However, fusion at this level is
difficult to achieve in practice because (i) the feature sets
of the various modalities may not be compatible (e.g.,
eigen-coefficients of face and minutiae set of finger), and
(ii) most commercial biometric systems do not provide
access to the feature sets (nor the raw data) which they use
in their products. Fusion at the decision level is considered
to be rigid due to the availability of limited information.
Thus, fusion at the match score level is usually preferred,
as it is relatively easy to access and combine the scores
presented by the different modalities.
Many biometric traits including fingerprint [56],
palmvein [57], finger surface [58],[59],[60], face
[61],[62],[63],[64], iris [65] and hand shape
[66],[67],[68],[69],[70] have been combined with
palmprints at score level or at representation level.
Although fusion increases accuracy, it generally increases
computational costs and template sizes.
6. Conclusion
In this paper we have reviewed the various
existing methods used for palm print verification system.
117 Krishneswari and Arumugam

We recommend D.Zhang et al. work [5] for palm print
acquisition which uses CCD based scanner. We also
recommend Kong’s PhD thesis because it contains palm
code, fusion code, competitive code and the theory of
coding method. We suggest Adams Kong et al.
competitive coding scheme for palm print verification
[54].Palm print recognition is an emerging field and only
limited works were carried out which paves way for the
researchers to invent new methods to reduce the error
rates and to improve the accuracy and speed of the system.
References
[1]
Biometrics: personal identification in networked
society, A.Jain, R.Bolle and S.Pankanti; eds.Boston:
Kluwer Academic, 1999.
[2] D.Zhang, Palmprint Authentication, Norwell,
mass.Kluwer Academic publishers, 2004.
[3] W.K. Kong, D. Zhang, Palmprint texture analysis
based on low-resolution images for personal
authentication, in: Proceedings of 16th International
Conference on Pattern Recognition, vol. 3, 2002, pp.
807–810.
[4] A. Kong, D. Zhang, G. Lu, A study of identical twins
palmprint for personal verification, Pattern
Recognition 39 (11) (2006) 2149–2156.
[5] D. Zhang, W.K. Kong, J. You, M. Wong, On-line
palmprint identification, IEEE Transactions on
Pattern Analysis and Machine Intelligence 25 (9)
(2003) 1041–1050.
[6] T. Connie, A.T.B. Jin, M.G.K. Ong, D.N.C. Ling, An
automated palmprint recognition system, Image and
Vision Computing 23 (5) (2005) 501–515.
[7] C.C. Han, A hand-based personal authentication using
a coarse-to- fine strategy, Image and Vision
Computing 22 (11) (2004) 909–918.
[8] C.C. Han, H.L. Cheng, C.L. Lin, K.C. Fan, Personal
authentication using palm-print features, Pattern
Recognition 36 (2) (2003) 371–381..
[9] X. Wu, D. Zhang, K. Wang, Fisher palms based
palmprint recognition, Pattern Recognition Letters 24
(15) (2003) 2829–2838.
[10] G. Lu, D. Zhang, K. Wang, Palm print recognition
using eigenpalms features, Pattern Recognition
Letters 24 (9) (2003) 1463–1467.
[11] L. Zhang, D. Zhang, Characterization of palm prints
by wavelet signatures via directional context
modeling, IEEE Transactions on Systems, Man and
Cybernetics, Part B 34 (3) (2004) 1335–1347.
[12] J. You, W.K. Kong, D. Zhang, K.H. Cheung, On
hierarchical palmprint coding with multiple features
for personal identification in large databases, IEEE
Transactions on Circuits and Systems for Video
Technology 14 (2) (2004) 234–243.
[13] W. Li, D. Zhang, Z. Xu, Palmprint identification by
Fourier transform, International Journal of Pattern
Recognition and Artificial Intelligence 16 (4) (2002)
417–432.
[14] S. Ribaric, I. Fratric, A biometric identification system
based on Eigenpalm and Eigenfinger features, IEEE
Transactions on Pattern Analysis and Machine
Intelligence 27 (2005) 1698–1709.
[15] S.Litsuka,k.Miyasava,T.Aoki,A Palmprint recognition
algorithim using Principal Component Analysis of
phase information
[16] X. Wu, D. Zhang, K. Wang, B. Huang, Palmprint
classification using principal lines, Pattern
Recognition 37 (10) (2004) 1987–1998.
[17] F. Li, M.K.H. Leung, X. Yu, Palmprint identification
using Hausdorff distance, in: Proceedings of
International Workshop on Biomedical Circuits and
Systems, 2004, pp. S3/3–S5-8.
[18] G. Lu, K. Wang, D. Zhang, Wavelet based feature
extraction for palmprint identification, in: Proceeding
of Second International Conference on Image and
Graphics, 2002, pp. 780–784
[19] X. Wu, K. Wang, D. Zhang, Line feature extraction
and matching in palmprint, in: Proceeding of the
Second International Conference on Image and
Graphics, 2002, pp. 583–590.
[20] A. Kumar, D. Zhang, Combining fingerprint,
palmprint and hand- shape for user authentication, in:
Proceedings of the International Conference on
Pattern Recognition, vol. 4, 2006, pp. 549–552.
[21] J. Doublet, M. Revenu, O. Lepetit, Robust grayscale
distribution estimation for contactless palmprint
recognition, in: Proceedings of the First IEEE
International Conference on Biometrics: Theory,
Applications, and Systems, 2007, pp. 1–6.
[22] Q. Li, Z. Qiu, D. Sun, Feature-level fusion of hand
biometrics for personal verification based on Kernel
PCA, International Conference on Biometrics, 2006,
pp. 744–750.
[23] C. Poon, D.C.M. Wong, H.C. Shen, A new method in
locating and segmenting palmprint into region-of-
interest, in: Proceedings of the 17th International
Conference on Pattern Recognition, vol. 4, 2004, pp.
533–536.
[24] G.M. Lu, K.Q. Wang, D. Zhang, Wavelet based
independent component analysis for palmprint
identification, in: Proceedings of International
Conference on Machine Learning and Cybernetics, vol.
6, 2004, pp. 3547–3550.
[25] P. Hennings, B.V.K.V. Kumar, Palmprint recognition
using correlation filter classifiers, in: Conference
Record of the 38th Asilomar Conference on Signal,
Systems and Computers, vol. 1, 2004, pp. 567–571.
[26] X. Wu, K. Wang, D. Zhang, Fuzzy direction element
energy feature (FDEEF) based palmprint identification,
in: Proceedings of International Conference on Pattern
Recognition, vol. 1, 2002, pp. 95–98.
118
A Review on Palm Print Verification System

[27] X. Wu, K. Wang. D. Zhang, Palmprint recognition
using directional energy feature, in: Proceedings of
International Conference on Pattern Recognition, vol.
4, 2004, pp. 475–478.
[28] X. Wu, K. Wang, D. Zhang, HMMs based palmprint
identification, in: Lecture Notes in Computer Science,
Springer, vol. 3072, 2004, pp. 775–781.
[29] X. Wu, K. Wang, D. Zhang, Wavelet based palmprint
recognition, in: Proceedings of the First International
Conference on Machine Learning and Cybernetics, vol.
3, 2002, pp. 1253–1257
[30] W.W. Boles, S.Y.T. Chu, Personal identification using
images of the human palms, in: Proceedings of IEEE
Region 10 Annual Conference, Speech and Image
Technologies for Computing and Telecommunications,
vol. 1, 1997, pp. 295–298
[31] M. Rafael Diaz, C.M. Travieso, J.B. Alonso, M.A.
Ferrer, Biometric system based in the feature of hand
palm, in: Proceedings of 38th Annual International
Carnahan Conference on Security Technology, 2004,
pp. 136–139.
[32] J.S. Noh, K.H. Rhee, Palmprint identification
algorithm using Hu invariant moments and Otsu
binarization, in: Proceeding of Fourth Annual ACIS
International Conference on Computer and Information
Science, 2005, pp. 94–99.
[33] K.Dong, G.Feng, D.Hu, Digital curvelet transform for
palmprint recognition, in: lecture notes in computer
science, Springer, vol, 3338, 2004, pp.639-645
[34] Z. Sun, T. Tan, Y. Wang, S.Z. Li, Ordinal palmprint
representation for personal identification, in:
Proceeding of Computer Vision and Pattern
Recognition, vol. 1, 2005, pp. 279–284.
[35] X. Wu, D. Zhang, K. Wang, Fusion of phase and
orientation information for palmprint authentication,
Pattern Analysis and Applications 9 (2) (2006) 103–
111..
[36] S.Y. Kung, S.H. Lin, M. Fang, A neural network
approach to face/palm recognition, in: Proceedings of
IEEE Workshop Neural Networks for Signal
Processing, 1995, pp. 323–332.
[37] P.A. Recobos Rodrigues, J.D. Landa Silva, Biometric
identification by dermatoglyphics, in: Proceedings of
International Conference on Image Processing, vol. 1,
1996, pp. 319–322.
[38] X. Wu, K. Wang, D. Zhang, A novel approach of
palm-line extraction, in: Proceeding of the Third
International Conference on Image and Graphics, 2004,
pp. 230–233
[39] A. Kumar, D. Zhang, Integrating shape and texture for
hand verification, in: Proceedings of Third
International Conference on Image and Graphics, 2004,
pp. 222–225.
[40] A. Kumar, H.C. Shen, Palmprint identification using
PalmCodes, in: Proceedings of 3rd International
Conference on Image and Graphics, 2004, pp. 258–261.
[41] N. Duta, A.K. Jain, K.V. Mardia, Matching of
palmprints, Pattern Recognition Letters 23 (4) (2002)
477–485.
[42] W. Shu, D. Zhang, Automated personal identification
by palmprint, Optical Engineering 38 (8) (1998) 2359–
2362.
[43] X. Wu, K. Wang, D. Zhang, Palmprint authentication
based on orientation code matching, in: Proceeding of
Fifth International Conference on Audio- and Video-
based Biometric Person Authentication, 2005, pp. 555–
562
[44] J.S. Chen, Y.S. Moon, H.W. Yeung, Palmprint
authentication using time series, in: Proceeding of
Fifth International Conference on Audio- and Video-
based Biometric Person Authentication, 2005, pp. 20–
22.
[45] T. Savic, N. Pavesic, Personal recognition based on an
image of the palmar surface of the hand, Pattern
Recognition 40 (2007) 3152–3163.
[46] D.S. Huang, W. Jia, D. Zhang, Palmprint verification
based on principal lines, Pattern Recognition 41 (4)
(2008) 1316–1328.
[47] W. Jia, D.S. Huang, D. Zhang, Palmprint verification
based on robust line orientation code, Pattern
Recognition 41 (5) (2008) 1504–1513..
[48] R. Chu, Z. Lei, Y. Han, S.Z. Li, Learning Gabor
magnitude features for palmprint recognition, ACCV,
2007, pp. 22–31.
[49] X. Zhou, Y. Peng, M. Yang, Palmprint recognition
using wavelet and support vector machines, in: The
Ninth Pacific Proceeding of IEEE Interna c Rim
International Conference on Artificial Intelligence
Guilin, China, 2006, pp. 285–393.
[50] M. Wong, D. Zhang, W.K. Kong, G. Lu, Real-time
palmprint acquisition system design, IEEE
Proceedings, Vision and Signal Processing 152 (5)
(2005) 527–534.
[51] L. Zhang, Z. Guo, Z. Wang, D. Zhang, Palmprint
verification using complex wavelet transform, in:
Proceedings of International Conference on Image
Processing, vol. 2, 2007, pp. 417–420.
[52] J. Canny, A computational approach to edge detection,
IEEE Transactions on Pattern Analysis and Machine
Intelligence 8 (6) (1986) 450–463.
[53] A.Kumar, D.C.M.Wong, H.C shen, A.K Jain, personal
verification using hand geometry biometric in: Lecture
notes in computer science,springer, 2003,pp.668-678.
[54] A.W.K. Kong, D.Zhang, Competitive coding scheme
for Palm print verification in: proceedings of
international conference on Pattern Recognition,
vol.1.2004, pp520-523.
[
55] G.Y.chen, T.D.Bui, a.krzyak, Palmprint classification
using dual –tree complex wavelets, in: proceedings of
international conference on image Processing, 2006,pp
2645-2648.
119 Krishneswari and Arumugam

[56] R.K. Rowe, U. Uludag, M. Demirkus, S. Parthasaradhi,
A.K. Jain, A multispectral whole-hand biometric
authentication system, in: Proceedings of Biometric
Symposium Biometric Consortium Conference,
Baltimore, September, 2007.
[57] J.G. Wang, W.Y. Yau, A. Suwandy, E. Sung, Person
recognition by fusing palmprint and palm vein images
based on Laplacianpalm representation, Pattern
Recognition 41 (5) (2008) 1514–1527.
[58] S. Ribaric, I. Fratric, A biometric identification system
based on Eigenpalm and Eigenfinger features, IEEE
Transactions on Pattern Analysis and Machine
Intelligence 27 (2005) 1698–1709.
[59] Q. Li, Z. Qiu, D. Sun, Feature-level fusion of hand
biometrics for personal verification based on Kernel
PCA, International Conference on Biometrics, 2006,
pp. 744–750.
[60] T. Savic, N. Pavesic, Personal recognition based on an
image of the palmar surface of the hand, Pattern
Recognition 40 (2007) 3152–3163.
[61] S. Ribaric, I. Fratric, K. Kis, A biometric verification
system based on the fusion of palmprint and face
features, in: Proceeding of the Fourth International
Symposium on Image, Signal and Signal Processing
and Analysis, 2005, pp. 15–17.
[62] A. Kumar, D. Zhang, Integrating palmprint with face
for user authentication, in: Proceedings of Multi Modal
User Authentication Workshop, 2003, pp. 107–112.
[63] G. Feng, K. Dong, D. Hu, D. Zhang, When face are
combined with palmprints: a novel biometric fusion
strategy, in: Lecture Notes in Computer Science,
Springer, vol. 3072, 2004, pp. 701–707.
[64] X.Y. Jing, Y.F. Yao, D. Zhang, J.Y. Yang, M. Li,
Face and palmprint pixel level fusion and Kernel
DCV-RBF classifier for small sample biometric
recognition, Pattern Recognition 40 (2007) 3209–3224.
[65] X. Wu, D. Zhang, K. Wang, N. Qi, Fusion of palmprit
and iris for personal authentication, in: Proceedings of
the Third International Conference on Advanced Data
Mining and Applications, Harbin, China, 2007.
[66] S. Ribaric, D. Ribaric, N. Pavesic, Multimodal
biometric user-identification system for network-based
applications, IEEE Proceedings, Vision, Image and
Signal Processing 150 (6) (2003) 409–416.
[67] Q. Li, Z. Qiu, D. Sun, Feature-level fusion of hand
biometrics for personal verification based on Kernel
PCA, International Conference on Biometrics, 2006,
pp. 744–750.
[68] A. Kumar, D.C.M. Wong, H.C. Shen, A.K. Jain,
Personal verification using palmprint and hand
geometry biometric, in: Lecture Notes in Computer
Science, Springer, 2003, pp. 668–678.
[69] A. Kumar, D. Zhang, Integrating shape and texture for
hand verification, in: Proceedings of Third
International Conference on Image and Graphics, 2004,
pp. 222–225.
[70] A. Kumar, D.C.M. Wong, H.C. Shen, A.K. Jain,
Personal authentication using hand images, Pattern
Recognition Letters 27 (13) (2006) 1478–1486.
Author Biographies
Mrs.K.Krishneswari completed her B.E
(computer science and engineering) in 2000 from
tamilnadu college of engineering under
Bharathiyar University, Coimbatore. M.E.
(software engineering) in 2005 from sri
ramakrishna engineering college under Anna
University, Chennai. Currently she is pursuing
Ph.D degree from Anna University, Coimbatore.
She is working as an assistant professor, department of computer
science and engineering at tamilnadu college of engineering, Coimbatore.
She is a member of CSI, ISTE. Her research areas include Image
processing, Pattern recognition, Biometrics and having 8 years of
teaching experience in engineering colleges.
Dr.S.Arumugam obtained his B.E. degree in
electrical and electronics engineering from the
University of Madras in 1971, his M.Sc. (Engg.)
degree in applied electronics from University of
Madras in 1973 and Ph.D. in computer science and
engineering from Anna University in 1983. He has
a distinguished career in teaching and research for
more than 37 years. In the year 1974, he joined the
Technical Education Service of tamilnadu
government as associate lecturer in college of engineering, Guindy.
Going up the rungs of the ladder, he was elevated as principal in 1998
and served at government college of engineering, Bargur and
government college of technology, Coimbatore. In 2005, he assumed
charge as additional director of technical education and chairman, Board
of Examinations, Chennai. He retired from service in 2007. Presently he
is working as chief executive officer in nandha educational institutions,
Erode. He has successfully guided 12 research scholars for their Ph.D.
and currently he is guiding 23 research scholars. He has published more
than 100 papers in national and international conferences and journals.
He is a member in IEEE, ISTE, FIE (I), FIETE and SMCSI.
120
A Review on Palm Print Verification System