50120130405034

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME

TECHNOLOGY (IJCET)

ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 4, Issue 5, September – October (2013), pp. 292-299
© IAEME: www.iaeme.com/ijcet.asp
Journal Impact Factor (2013): 6.1302 (Calculated by GISI)
www.jifactor.com

IJCET
©IAEME

REVIEW ON PALMPRINT RECOGNITION METHODS
Ms. Sneha Vivek Sonawane
Department of Computer Engineering
MPSTME, SVKM’s NMIMS, Shirpur, Maharashtra, India
Dr. M. V. Deshpande
Ms. Arundhati Sahoo

ABSTRACT
Palmprint based recognition is becoming very popular now a days as palmprint provides
features like principal lines, minutiae features, ridge orientation and creases. These features are very
helpful for verification or the identification of an individual. Most of the research has been done in
palmprint recognition due to its stability, reliability and uniqueness. Moreover it has been used for
law enforcement, civil applications and access control applications. Researchers have proposed a
variety of palmprint preprocessing, feature extraction and matching approaches. This paper provides
a review of palmprint recognition approaches and analysis of methods.
Keywords: Biometrics, Palmprint Recognition, Palmprint Features.
I. INTRODUCTION
Biometrics system refers to the identification of an individual by means of their unique
physiological and behavioral characteristics [11]. Biometrics system is basically used for
identification purpose and also for security purpose like access control. There are different types of
modalities available for identification purpose such as iris, fingerprints, palmprint, face etc [13],
where iris and fingerprint modalities are widely used in biometrics system as these two modalities are
most reliable and possess uniqueness. Palmprint is also one of the reliable modality since it possess
292


more features than that of the other modality such as principal lines, orientation, minutiae, singular
points etc. Also palmprint modality is unique for each individual [13], moreover it is universal.
Palmprint recognition is used in civil applications, law enforcement and many such applications
where access control is essential.
Palm has features like geometric features, delta point’s features, principal lines features,
minutiae, ridges and creases [13]. Principal lines are namely heart line, head line and life line [12].
Figure 1 shows structure of palmprint. Palmprint contains three principal lines which divides palm
into three regions: Interdigital, Hypothenar and Thenar. An Interdigital region lies above the Heart
line. The Thenar lies below the Life line. And Hypothenar is between Heart and Life line. From
palmprint principal lines, minutiae, ridges features can be extracted for identification.
Palmprint recognition techniques have been grouped into two main categories [8], first
approach is based on low-resolution features and second approach is based on high-resolution
features. First approach make use of low-resolution images (such as 75 or 150 ppi), where only
principal lines, wrinkles, and texture are extracted [5]. Second approach uses high resolution images
(such as 450 or 500 ppi) [1] [8], where in addition to principal lines and wrinkles, more discriminant
features like ridges, singular points, and minutiae can be extracted.

Figure 1. Structure of palmprint (principal lines, ridges, creases and minutiae in a palmprint)
The rest of this paper is organized as follows: In Section II the architecture of palmpriint
recognition system is introduced. Section III describes the related work of palmprint recognition.
Discussion and analysis is made in section IV. Finally, we make conclusions in Section V.
II. ARCHITECTURE OF PALMPRINT RECOGNITION
Palmprint based recognition can operate in either identification or verification mode.
Palmprint identification refers to one-to-many match, means input palmprint of an individual is
matched with all templates present in database. It conform the identity of an individual. Palmprint
verification refers to one-to-one match, means input palmprint of an individual is matched with its
own templates present in database for which an individual claims [11].
Palmprint recognition system basically follows four steps that are image acquisition,
preprocessing, feature extraction and matching. Figure 2 shows general block diagram of palmprint
recognition system.
293


Figure 2. General block diagram of palmprint recognition system
Image Acquisition
In this phase, image of palmprint is first capture with the help of different types of digital
cameras. Acquired image may be blurred or it may have noise, which decreases the quality of an
image and affects the performance rate of palmprint recognition system directly. The plamprint
image acquired may vary by position, direction and stretching degree [13].
A.

Pre-processing
After capturing the data or image of the palmprint, preprocessing is formed on image.
Sometimes noise is present in the captured image, noise can be remove with help of filters in
processing phase. Images need to be normalized; this is also done in preprocessing phase. This
phase need not necessary to accomplish.
B.

Feature Extraction
Feature extraction is followed by pre-processing. In feature extraction phase features of palm
are extracted like principal lines, orientation field, minutiae, density map, texture, singular points etc.
These features are helpful for identification or verification of individual. Extracted features are stored
in database for further process of matching.
C.

Matching
Matching is next to the feature extraction phase. Feature matching determines the degree of
similarity of recognition template with master template [13]. Different approaches are used for
matching. Input provided by individual is matched with templates present in database. Matching is
dependent on whether the system performs identification or verification. If it performs identification
then one-to-many matching, which matches input as palmprint of individual with all templates of
database otherwise one-to-one match is done for verification, where input of an individual is
matched with only the template he/she claims to be.
D.

III. RELATED WORK
In the last few years, a lot of research has been done in the palmprint recognition area, where
researchers have proposed many methods for palmprint recognition. G. Lu, D. Zhang and K. Wang
[14] has proposed palmprint recognition using eigenpalms features, where original image of
294


palmprint is transformed into the feature space i.e. eigenpalm with the help of K-L transform.
Euclidean distances classifier is used for matching of recognition template and master template.
Cappelli, Ferrara, and Maio [1] proposed high resolution palmprint recognition system which
is based on minutiae extraction. Pre-processing is formed by segmentation of an image from its
background. To enhance the quality of image, local frequencies and local orientations are estimated.
Local orientation is estimated using fingerprint orientation extraction approach and local frequencies
are estimated by counting the number of pixels between two consecutive peaks of gray level along
the direction normal to local ridge orientation. Minutiae feature is extracted in feature extraction
phase. To extract the minutiae features contextual filtering with Gabor filters approach is applied.
Minutiae cylinder code has been used for matching the minutiae features.
Kong, Zhang and Li [2] have proposed palmprint verification using 2-DGabor filter. 2-D
Gabor filters for feature extraction from palmprint. In the pre-processing of an image low pass filter
and boundary tracking algorithm is applied. Texture feature is extracted using the texture-based
feature extraction technique which uses the 2-D Gabor Filter. Palmprint matching is based on
normalized hamming distance.
Huanga, Jiaa and Zhang [3] have proposed the palmprint verification system based on
principal line extraction. Modified finite Radon transform has been used for feature extraction. A
feature considered is principal lines. For matching of test image with a training image the line
matching technique has been used which is based on pixel-to-area algorithm.
Zhang, Kong, You and Wong [4] have proposed Online Palmprint Identification. The
proposed system takes online palmprints, and uses low resolution images. Low pass filter and
boundary tracking algorithm is used in pre-processing phase. Circular Gabor filter used for feature
extraction and 2-D Gabor phase coding is used for feature representation. A normalized hamming
distance is applied for matching.
Konga, Zhanga, and Kamel [5] have proposed palmprint identification using feature level
fusion. Multiple elliptical Gabor filters with different orientations are used to extract the phase
information. Phase information is then merged according to a fusion rule to produce a single feature
called the Fusion Code. Matching of two Fusion Codes is measured by normalized hamming
distance.
Jiaa, Huanga and Zhang [6] have proposed palmprint verification based on robust line
orientation code. Modified finite Radon transform has been used for feature extraction, which
extracts orientation feature. For matching of test image with a training image the line matching
technique has been used which is based on pixel-to-area algorithm.
Prasad, Govindan and Sathidevi [7] have proposed Palmprint Authentication Using Fusion of
Wavelet Based Representations. Features extracted are Texture feature and line features. In proposed
system pre-processing includes low pass filtering, segmentation, location of invariant points, and
alignment and extraction of ROI. OWE used for feature extraction. The match scores are generated
for texture and line features individually and in combined modes. Weighted sum rule and product
rule is used for score level matching.
Dai and Zhou [8] introduces high resolution approach for palmprint recognition with multiple
features extraction. Features like minutiae, density, orientation, and principal lines are taken for
feature extraction. For orientation estimation the DFT and Radon-Transform-Based Orientation
Estimation are used. For minutiae extraction Gabor filter is used for ridges enhancement according to
the local ridge direction and density. Density map is calculated by using the composite algorithm,
Gabor filter, Hough transform. And to extract the principal line features Hough transform is applied.
SVM is used as the fusion method for the verification system and the proposed heuristic rule for the
identification system.
As the problem of distortion due to many creases on palmprint and discrimination power of
different regions of palmprint Dai, Feng, Zhou [10] introduces segment-based palmprint matching
295


and fusion algorithm, where whole palmprint image is divided into different regions and then each
region is separately matched to deal with distortion. The similarity of two palmprints is calculated by
fusing the similarity scores of different segments using a Bayesian framework. Dai, Feng, Zhou [10]
also an orientation field-based registration algorithm is used to reduce the computational complexity.
P.S. Sanjekar [16] used haar wavelet for palmprint identification which is image based method. For
matching difference vector formula is used and features extracted are standard deviation and mean.
IV.DISSCUSION AND ANALYSIS
Researchers have introduced different methods for palmprint recognition. The analysis of
time required to execute these methods, FRR and ERR is done in this section. G. Lu, D. Zhang and
K. Wang [14] used 3096 sample images of palmprints and the feature recognition rate achieved is
99.19%. The PolyU Palmprint Database contains 7752 grayscale images of 386 different palms. The
resolution of all the original palmprint images is 384 × 284 pixels at 75 dpi [6]. Time required for
pre-processing, feature extraction and matching is 316ms, 70ms, 3.9ms respectively. Total execution
time is 389.9 ms.
Hong Kong Polytechnic University (PolyU) Palmprint Database contains 7752 gray scale
images, corresponding to 386 different palms. The resolution of all the original palmprint images is
384 × 284 pixels and75 dpi images of user are used. The total execution time required is about 0.7 s.
Gabor filter cannot correctly detect the directional energies of wide lines in image but MFRAT is
basically used to detect directional energies of wide lines therefore, MFRAT is used by [3].
Palmprint images from 193 individuals are captured and 75 dpi resolution images of user are
used [4]. The execution time for the pre-processing, feature extraction and matching are 538 ms, 84
ms, and 1.7 ms, respectively. The total execution time is about 0.6 seconds. Palmprint images from
284 individuals are capture, resulting in a total number of 11,074 images from 568 different palms.
The size of images used is 384 × 284 with a resolution of 75 dpi [5]. Time required for preprocessing, feature extraction and for matching is 267 ms, 123 ms, 18ms respectively. The total
identification time is 0.41 s and for verification 0.39 s is requires.
PolyU-online-palmprint-database is used [7]. This database contains low resolution
palmprints, these are 75 dpi resolution. The equal error rate obtained is 1.37%. Thus, fusion of line
and texture features can reduce the EER significantly by 39.38% at minimal computational burden.
Tsinghua Palmprint Database (THUPALMLAB) is used [1]. It contains 1280 palmprint images from
80 different subjects (left and right palms of each subject, eight impressions per palm). Images were
captured using a palmprint scanner from Hisign; the image size is 2040 × 2040 pixels, the resolution
500 dpi which are high resolution images. Equal Error Rate (ERR) achieved is less than 0.01.
Average time required to extract the all features is 1.935 s and 0.038s are required for matching.
Minutiae Cylinder-Code is efficient against skin distortion; therefore it gives more accurate result
than other matching techniques. But the proposed algorithm takes more time to minutiae extraction
and orientation extraction.
Dai and Zhou [8] uses THUPALMLAB for palmprint recognition. Image used for
recognition is of size 2040 × 2040 pixels with 500 ppi resolution. False rejection rate for verification
is 16% and achieved identification partial palmprint recognition rate is 91.7%. Wai Kin Kong, David
Zhang, W. Li [2] database contains 4647 palmprint images collected from 120 individuals. Images
captured are of two resolutions: 384×284 and 768×568 and 75 ppi images of user are used these are
very low resolution images which directly affects the performance.. The texture features gives
accuracy of FRR or FAR lesser than that of the minutiae, ridges or principal line features. Palmprint
can be combining with other modality to increase the performance of [15].

296


Table 1 show the analysis of palmprint recognition where different methods for feature
extraction and for matching are used by authors. Also it shows that, which features are extracted for
matching at the time of feature extraction and analysis of time required for execution, EER, FRR.
Table -1 Literature Analysis
Author

Features
Extracted

G. Lu, D. Zhang
and K. Wang [14]

Eigenpalm

Jiaa, Huangaand,
Zhang [6]
Huanga, Jiaa,
Zhang [3]

Principal Lines

Zhang, Kong,
You, Wong [4]

Feature
Extraction
Method
K-L Transform

Matching Method

Remark

Euclidean
Distance Classifier

Feature
Recognition rate
99.19% ,
Total execution
time is 389.9 ms
Total execution
time is about 0.7
s
The total
execution time is
about 0.6 s
Total
identification
time is 0.41 s
and for
verification 0.39
s is requires.
EER is 1.37%.

Modified finite
Radon transform
Modified finite
Radon transform

Line matching
technique
Line matching
technique

Texture features

Circular Gabor
filter

Normalized
hamming distance

Konga, Zhanga,
Kamel [5]

Phase information
or Fusion Code

Multiple elliptical
Gabor filters

Normalized
hamming distance

Prasad, Govindan,
Sathidevi [7]

Texture feature
and principal line

OWE

Cappelli, Ferrara,
Maio [1]

Minutiae

Contextual
filtering with
Gabor filters

Weighted sum
rule and product
rule
Minutiae cylinder
code

Dai and Zhou [8]

minutiae, density,
orientation, and
principal lines

Composite
algorithm , Gabor
filter, Hough
transform

Principal Lines

Support Vector
Machine, heuristic
rule

ERR less than
0.01 and Total
execution time
required is
1.973s
FRR for
verification is
16% and for
identification
partial
palmprint
recognition rate
is 91.7%.

V. CONCLUSION
Summarizing we can say that palmprint recognition is highly reliable modality than other one
as it possesses choice of features like minutiae, principal lines, density map, ridges, and creases, delta
point. Palmprint is unique for all the users. Palmprint recognition has various phases as image preprocessing, feature extraction and matching. Researchers worked on low resolution images of
palmprint. A low resolution image directly affects the recognition rate of user as low resolution
images unable to provide the most reliable feature like minutiae. High resolution images provide
297


more discriminent features which affect the recognition rate of user. Extracting more than one
feature makes palmprint recognition reliable and also increases the recognition rate.
The future work can be extended to apply gaussianization, the feature normalization method
on the high resolution images where multiple features can be extracted. Principal component analysis
PCA based gaussianization normalizes the individual components of the extracted feature vectors of
user so the normalized vectors are better suited for classification. Dimensional feature vector
transforms the feature components in such a way that, their distribution approximates the normal
distribution with a predefined mean and standard deviation. Feature normalization on high resolution
images reduce the time required for matching the extracted features of user with the master template
and also reduce the error rate of FAR, FRR.
VI. REFERENCES
[1]

[2]
[3]
[4]

[5]
[6]
[7]
[8]

[9]
[10]

[11]
[12]
[13]
[14]
[15]
[16]

R. Cappelli, M. Ferrara, and D. Maio, “A Fast and Accurate Palmprint Recognition System
Based on Minutiae,” IEEE Transaction on System, Man and Cybernetics- Part B:
Cybernetics, Vol. 42, No. 3, pp. 1083-4419, June 2012.
W. K. Kong, D. Zhang, W. Li, “Palmprint feature extraction using 2-D Gabor Filters,”
Pattern Recognition, Elesvier, pp. 2339 – 2347, 2003.
Huang,W. Jia, D. Zhang, “Palmprint verification based on principal lines,” Pattern
Recognition, Science Direct, pp.1316 – 1328, 2008.
D. Zhang, W. K. Kong, J. You, M. Wong, “Online Palmprint Identification,” IEEE
Transaction on Pattern Analysis and Machine Intelligence, Vol.25, No. 9, pp. 0162-8828,
Sept 2003.
A. Konga, D. Zhang, M. Kamel, “Palmprint identification using feature-level fusion,”
Pattern Recognition, Science Direct, pp. 478 – 487, 2006.
Huang,W. Jia, D. Zhang, “Palmprint verification based on robust line orientation code,”
Pattern Recognition, Science Direct, pp. 1504 – 1513, 2008.
S. M. Prasad, V. K. Govindan , P. S. Sathidevi, “Palmprint Authentication Using Fusion of
Wavelet Based Representations,” IEEE, pp. 978-1-4244-5612-3, 2009.
J. Dai and J. Zhou, “Multifeature-Based High-Resolution Palmprint Recognition,” IEEE
May 2011.
A. Kong, D. Zhang, and M. Kamel, “A survey of palmprint recognition,” Pattern
Recognition, Elesvier, vol. 42, no. 7, pp. 1408–1418, July 2009.
J. Dai, J. Feng, J. Zhou, “Robust and Efficient Ridge-Based Palmprint Matching,” IEEE
August 2012.
http://www.cse.iitk.ac.in/users/biometrics/pages/what_is_biom_more.htm
X.Q. Wu, D. Zhang, K.Q. Wang, B. Huang, “Palmprint classification using principal lines,”
Pattern Recognition, Science Direct, pp. 1987–1998, 2004.
A. Jain, P. Flynn, and A. Ross, Handbook of Biometrics. Springer, 2007.
G. Lu, D. Zhang and K. Wang, “Palmprint recognition using eigenpalms features,” Pattern
Recognition, Science Direct, pp. 1987–1998, 2003.
P.S. Sanjekar, J.B. Patl, “An Overview of Multimodal Biometrics,” International Journal of
Signal and Image Processing, Vol.4, No. 1, Feb 2013.
P.S. Sanjekar, “Palmprint Identification by Wavelet Transform,” Proc. Of international
conf. on Image Processing and vision System, Vol 1, Oct 2011.

298

AUTHOR’S PROFILE

Ms. Sneha Vivek Sonawane is pursuing M.Tech at Mukesh Patel School
of Technology Management & Engineering, Shirpur Campus, Dist. Dhule
(Maharashtra) of SVKM’s NMIMS (Deemed to be University).

Dr. Manojkumar Deshpande is Professor & Associate Dean of Mukesh
Patel School of Technology Management and Engineering, Shirpur Campus,
Dist. Dhule (Maharashtra) of SVKM’s NMIMS (Deemed to be University). He
has published research papers related to Software Engineering, Intelligent
Systems, Software Estimation, Soft Computing and Multimedia Systems. He is
guiding post graduate students for projects. He has been listed as a guide for
Ph.D. in Computer Engineering at SVKM’s NMIMS.

Ms. Arundhati Sahoo is Assistant Professor at Mukesh Patel School of
Technology Management and Engineering , Shirpur Campus, Dist. Dhule
(Maharashtra) of SVKM’s NMIMS ( Deemed to be University).She is acting as a
Co-guide for Post graduate students for projects. She has published research
papers related to Image Processing Fabric Defect Detection by Moment feature,
DCT and DFT method.

299

50120130405034

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (18)

Similaire à 50120130405034

Similaire à 50120130405034 (20)

Plus de IAEME Publication

Plus de IAEME Publication (20)

Dernier

Dernier (20)

50120130405034