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TECHNIA – International Journal of Computing Science and Communication Technologies, VOL. 3, NO. 2, Jan. 2011. (ISSN 0974-3375)
Palmprint Recognition Using Image Processing
1K.Y. Rajput, 2Melissa Amanna, 3Mankhush Jagawat, 4Mayank Sharma
Thadomal Shahani Engineering College, Mumbai, Maharashtra, India [email protected]
Abstract—In today’s world, biometrics system is used almost
everywhere for the security and personal recognition. The
palmprint is one of the most reliable physiological
characteristics that can be used to distinguish between
individuals. The primary objective of this paper is to present
a palmprint recognition system using minimum resources.
The system is implemented by means of the transforms used
in image processing. Palmprint recognition is implemented
using three algorithms namely Kekre’s Fast Codebook
Generation (KFCG), Discrete Cosine Transform (DCT) and
Fourier Descriptors (FD). The paper also deals with the
comparison of these techniques. All images in the data base
are converted to gray level images before processing.
Keywords- biometrics, palmprint, difference in strength,
Kekre’s algorithm, DCT, Fourier descriptors, Euclidean
distance.
I. INTRODUCTION
Today, in our daily life, we are often being asked for verification of our identity. Normally, this is done through the use of passwords when pursuing activities like domain accesses, single sign-on, application logon etc. In the process, the role of personal identification and verification becomes increasingly important in our society. With the onslaught of improved forgery and identity impersonation methods, previous ways of correct authentication are not sufficient. Therefore, new ways of efficiently proving the authenticity of an identity at a low cost are greatly needed. Various avenues have been explored to provide a solution and biometric-based identification is proved to be an accurate and efficient answer to the problem. Biometrics has been an emerging field of research in the recent years and is devoted to identification of individuals using physical traits, such as those based on iris or retinal scanning, face recognition, fingerprints, or voices[1-4]. As unauthorized users are not able to display the same unique physical properties to have a positive authentication, reliability will be ensured. This is much better than the current methods of using passwords, tokens or personal identification number (PINs) at the same time provides a cost effective convenience way of having nothing to carry or remember.
Although there are numerous distinguishing traits used for personal identification, this research will focus on using palmprints to more correctly and efficiently identify different personnel through classification at a low cost.
Palmprint is preferred compared to other methods such as fingerprint or iris because it is distinctive, easily captured by low resolution devices as well as contains additional features such as principal lines. With the help of palm geometry, a highly accurate biometric system can be designed. Iris input devices are expensive and the method
is intrusive as people might fear of adverse effects on their eyes.
Fingerprint identification requires high resolution capturing devices and may not be suitable for all as some may be finger deficient [5]. Palmprint is therefore suitable for everyone and it is also non-intrusive as it does not require any personal information of the user. Palmprint images are captured by acquisition module and are fed into recognition module for authentication. As shown in Fig.1, recognition module has many numbers of stages which are preprocessing, feature extraction, template extraction as well as matching with the database. This requires a large amount of time.
Fig. 1: General Block Diagram of a Biometric System
II. NEED FOR PALMPRINT TECHNOLOGY
Biometrics has been an emerging field of research in
the recent years and is devoted to identification of
individuals using physical traits, such as those based on
iris or retinal scanning, face recognition, fingerprints, or
voices. As unauthorized users are not able to display the
same unique physical properties to have a positive
authentication, reliability will be ensured.
Palmprint is preferred compared to other methods
such as fingerprint or iris because it is distinctive, easily
captured by low resolution devices as well as contains
additional features such as principal lines. Iris input
devices are expensive and the method is intrusive as
people might fear of adverse effects on their eyes.
Fingerprint identification requires high resolution
capturing devices and may not be suitable for all as some
may be finger deficient. Palmprint is therefore suitable for
everyone and it is also non-intrusive as it does not require
any personal information of the user. Palmprint images are
captured by acquisition module and are fed into
recognition module for authentication.
Compared with face recognition palmprint is
hardly affected by age and accessories.
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TECHNIA – International Journal of Computing Science and Communication Technologies, VOL. 3, NO. 2, Jan. 2011. (ISSN 0974-3375)
Compared with fingerprint recognition palmprint
images contain more information and needs only
low resolution image capturing devices which
reduces the cost of the system.
Compared with iris recognition the palmprint
images can be captured without intrusiveness as
people might fear of adverse effects on their eyes
and cost effective.
Hence it has become an important and rapidly
developing biometrics technology over the last decade.
Limited work has been reported on palmprint
identification and verification, despite the importance of
palmprint features. The system functions by projecting
palmprint images onto a feature space that spans the
significant variations among known images.
III. IMPLEMENTATION
A. Creating Database
The database has images of the size of 640 by 480
pixels. The image also contains background along with the
palm image. For our work only the principal lines are
required and the rest of the information must be
eliminated. The size of the image is not modified. It was
observed that the lines that were detected lie in the range
of 100 to 150 pixel values. For detection of the lines we
have made use of a special high frequency mask since
lines are high frequency areas. The data base consists of
palm images of eighteen persons. For each person there
are ten different samples with slight variation in
orientation. Out of the ten samples three are reserved for
training with which the remaining seven samples which
are the test images will be compared.
B. Techniques for Principal Line Extraction
The image taken as the test image is passed through
three algorithms. The algorithms used are Kekre‟s Fast
Codebook Generation, Discrete Cosine Transform and
Fourier descriptors.
C. Kekre’s Fast Codebook Generation (KFCG)
In this algorithm when the images are trained their
respective codebooks are generated. Here the feature
vector space has 8x4 numbers of elements. This is
obtained using following steps of Kekre‟s Fast Codebook
Generation (KFCG) algorithm. For each palm image in
data base a codebook is generated. Similarly a code book
of the palm image under test is generated. The codebook
of the test palm image is compared with each of the code
book of sample palm image in data base till the match is
found.
1. Code book generation of each of the sample palm
image in the data base
A palm image in data base is masked by applying
Difference In Strength (DIS) Mask.
The above masked Image is then divided into the
windows of size 2x2 pixels.
These windows are put in a row to get four values
per vector. Collection of these vectors is called as
a training set (initial cluster).
Mean (code vector) of the initial cluster is then
computed.
The first element of the training vector is
compared with the first element of the code
vector and the above cluster is split into two.
The mean of both the clusters is computed.
Both the clusters are split by comparing second
element of training vectors with second element
of the code vectors.
The process is repeated till the codebook of size
8x4 is obtained.
The result is stored as the codebook for the
image.
Thus the codebook of each palm image in the
database is generated.
2. Testing using KFCG
The test image of a particular individual is taken
from the user by means of a scanner.
The test image is then passed through a DIS
mask.
This image is then passed through the algorithm
to compute the codebook as mentioned above.
This codebook is then compared with the
generated codebooks of each of the palm image
in data base.
For comparison, the Euclidean distance between
the two codebooks (i.e. code book of palm image
under test and code book of one of the palm
image in database) is calculated and sorted.
The smallest Euclidean distance is compared with
the given threshold (which is statistically found
depending on required accuracy).
If the value is less than the threshold then the
image is taken as a match. Else the process of
comparison is repeated for rest of the palm
images in the database. If there is no match found
then the Person under test is declared as an illegal
entrant.
D. Discrete Cosine Transform (DCT)
In this algorithm when the images are trained, their
respective feature vectors are generated. There are two
feature vectors per image. This is obtained using following
steps of Discrete Cosine Transform (DCT) algorithm.
1. Feature vector generation using DCT
The palm image in data base is masked by
Difference In Strength (DIS) Mask.
The masked image is the subjected to
Thresholding followed by thinning (using 3x5
thinning mask ). This separates the principal lines
from rest of the lines on palm and also detected
principal lines are thinned.
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TECHNIA – International Journal of Computing Science and Communication Technologies, VOL. 3, NO. 2, Jan. 2011. (ISSN 0974-3375)
All rows of the image are scanned and whenever
a zero value (black colored) pixel is encountered
the row variable is incremented by 1.
DCT-2 is carried out of this row variable.
All columns of the image are scanned and
whenever a zero value (black colored) pixel is
encountered the column variable is incremented
by 1.
DCT-2 is carried out of this column variable.
Feature vector is generated using the row and
column DCT variable.
2. Testing using DCT
The palm image of a Person under test obtained
by the user by means of scanner is first passed
through a DIS mask followed by thresholding and
thinning.
DCT is carried on the above image and feature
vector is generated in the similar way as
explained above (in I-Feature Vector generation).
The feature vector of the test palm image is
compared with the feature vector of the sample
palm images.
For comparison, the Euclidean distance is
calculated and sorted.
The smallest Euclidean distance is compared with
the given threshold (which is statistically found
depending on required accuracy).
If the value is less than the threshold then that
image is taken as a match. Else the process of
comparison is repeated for rest of the palm
images in the database. If there is no match found
then the Person under test is declared as an illegal
entrant.
E. Fourier Descriptors (FD):
1. Feature descriptors generation using FFT
Fast Fourier Transform (FFT) of each of the palm
image under data base is obtained. After the FFT
operation, it is observed that majority of the signal energy
is carried by lower order coefficients and higher order
coefficients can be discarded to improve the speed of the
operation. This is obtained using the following steps of
Fourier Descriptor Algorithm:
Difference In Strength (DIS) Mask is applied on
the palm image.
Above Image is scanned and whenever a pixel
value greater than 200 is encountered, the x-
coordinate of that pixel is stored in „u‟ array and
the y-coordinate is stored in „v‟ array.
A complex variable „comz‟ is defined using u and
v arrays. comz = u + jv
FFT is carried out of this complex variable.
Feature vector is generated using only the lower
1024 FFT coefficients.
2. Testing using FDs
The palm image of a person under test obtained
by the user by means of a scanner is first passed
through a DIS mask followed by thresholding.
The above image is scanned and wherever a zero
pixel value is encountered its x- coordinate is
stored in the row array and the y-coordinate is
stored in the column array.
A complex variable is defined using the above
arrays.
FFT is performed on the above variable.
The Euclidean distance (i.e. FFT of palm image
under test and FFT of one of the palm image in
database) is calculated and sorted.
The smallest Euclidean distance is compared with
the given threshold (which is statistically found
depending on required accuracy).
If the value is less than the threshold then that
image is taken as a match. Else the process of
comparison is repeated for rest of the palm
images in the database. If there is no match found
then the Person under test is declared as an illegal
entrant.
IV. RESULTS AND ANALYSIS
A. Accuracy
The accuracy obtained by using DCT is 92.86%.
The accuracy obtained by using KFCG is 88.89%.
The accuracy obtained by using FOURIER
DESCRIPTORS is 68.25%.
TABLE I: ALGORITHM ACCURACY
Algorithm Accuracy (in %)
DCT 92.86
KFCG 88.89
Fourier Descriptors 68.25
V. USING OR LOGIC
The false accuracy rate observed is high. Hence to
reduce the false accuracy rate we make use of the OR
logic. In the OR logic an image is said to be recognized if
the feature vectors of the test image matches with that of
the trained images of the database.
A flag variable is set. A hit is said to take place when
the image is recognized by a particular algorithm. With
every hit the flag value is incremented. This process
significantly increases the accuracy rate. The accuracy
table II for different values of flag is as shown below.
TABLE II: FLAG VALUE VERSUS ACCURACY
Flag Value Accuracy (in %)
Flag >= 1 97.62
Flag >= 2 90.48
Flag >=3 63.49
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TECHNIA – International Journal of Computing Science and Communication Technologies, VOL. 3, NO. 2, Jan. 2011. (ISSN 0974-3375)
Fig. 2.1: Input Image of a Palm
Fig. 2.2: DIS Image of a Palm
Fig. 2.3: Thesholded Image of a Palm
Fig. 2.4: Masked Image of a Palm
VI. CONCLUSION
Thus we have observed that the palmprint recognition
system can be implemented using various transforms in
image processing. Today we are living in the information
age, where because of advent of the technology there is a
situation like information explosion. Images have giant
share in this information. More précised retrieval
techniques are needed to access the large image archives
being generated, for finding relatively similar images.
Here in this paper a novel image retrieval technique is
proposed. We have used KFCG algorithm to generate
codebook which is very fast as it does not involve any
Euclidian distance computation. This technique for CBIR
has far less complexity as compared to using full DCT.
The computational complexity of proposed method is 99%
less for the image with size of 640x480 compared to full
DCT. This results into only 1% time requirement per
query image for retrieval, as compared to full DCT. The
proposed technique avoids resizing of images for feature
extraction, which is necessary in case of applying any
transform technique directly on image. Using KFCG the
computation time is reduced significantly as compared to
DCT and Fourier Descriptors. We have also observed that
the overall accuracy increases by making use of the OR
logic. This system can be improved in future by
implementing it online.
REFERENCES
[1] L. Fang, M.K.H. Leung, T. Shikhare, V. Chan, K. F. Choon,
“Palmprint Classification”.
[2] Biometric Introduction, www.wikipedia.org/biometrics.
[3] D.K. Theckedath, “Image Processing Using MATLAB Codes
Fourth Edition”, Nandu Publications, 2009.
[4] R.C. Gonzalez, R.E. Woods, “Digital Image Processing Using
MATLAB”, Second Edition, Pearson Education, 2002.7.
[5] Palmprint database from http://www.cbsr.ia.ac.cn/CASIA%
20Palmprint%20Database/Palmprint.html.
[6] N. Salman “Image Segmentation Based on Watershed and Edge
Detection Techniques”.
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