Personal Identification and Verification using Palm Print Biometric

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International Journal of Latest Technology in Engineering Management and Applied Science
(IJLTEMAS), vol. 1, issue. X, December 2012
Personal Identification and Verification using Palm
print Biometric
Hlaing Htake Khaung Tin
University of Computer Studies, Yangon, Myanmar
hlainghtakekhaungtin@gmail.com
Abstract- Biometric technology is an efficient personal
authentication and identification technique. The biometric
identification system using human palm has been developed and
presented in this paper. In this paper, a new approach for
personal authentication using palm print images is discussed. The
proposed methodology is divided into four steps: image
acquisition by a regular webcam, image preprocessing for image
normalization, segmentation for biometric extraction and human
interpretation. Gray –Scale palm images are captured using a
digital camera at a resolution of 640*480. The proposed system
has been tested with different images. Experimental tests have
proved that the proposed approach is not only robust but also
quite efficient.
Keywords: Personal Identification, Verification, Biometrics,
Palm print.
I. INTRODUCTION
Biometrics helps to provide the identity of the user based on
his/her physiological or behavioral characteristics. The
physiological characteristics signifies using human body parts
for authentication like fingerprint, iris, ear, palm print, face
etc. The behavioral characteristics include action done using
body parts like voice, signature and gait etc. for
authentication. Authentication based on a token and password
etc. can be stolen or forgotten. Person’s friends or relatives
can easily access token and can guess the password. It is
necessary to add some features that can almost eliminate the
limitation of token-based and knowledge based methods [1]
Palm print is one of the relatively new physiological
biometrics due to its stable and unique characteristics. The
rich texture information of palm print offers one of the
powerful means in personal identification and verification.
Biometric palm print recognizes a person based on the
principal lines, wrinkles and ridges on the surface of the palm.
These line structures are stable and remain unchanged
throughout the life of an individual.
The inner surface of the normally contains three flexion
creases, secondary creases and ridges. The flexion creases are
also called principal lines and secondary creases are called
wrinkles. The flexion and the major secondary creases are
formed between the 3rd and 5th months of pregnancy [2] and
superficial lines appear after we born. Although the three
major flexions are genetically dependent, most of other
creases are not [3]. Even identical twins have different palm
prints. These non-genetically deterministic and complex
patterns are very useful in personal identification.
Human beings were interested in palm lines for fortune
telling long time ago. Scientists know that palm lines are
associated with some genetic diseases including Down
syndrome, Aarskog syndrome, Cohen syndrome and fetal
alcohol syndrome [4]. Scientists and fortunetellers name the
lines and regions in palm differently shown in Fig. 1 [5].
Fig. 1. Definitions of palm lines and regions from scientists
The rest of the paper is organized as follows: Section II
reviews palm print and the proposed system is shown in
Section III, Section VI lists identification and verification
system of the palm print. Finally, the conclusions are
presented in Section V.
II. PALM PRINT
There are two types of palm print recognition research, high
resolution and low resolution approaches. High resolution

International Journal of Latest Technology in Engineering Management and Applied Science
(IJLTEMAS), vol. 1, issue. X, December 2012
approach employs high resolution images while low resolution
approach employs low resolution images. High resolution
approach is suitable for forensic applications such as criminal
detection [6].
Low resolution images are more suitable for civil and
commercial applications such as access control. Generally
speaking, high resolution refers to 400 dpi or more and low
resolution refers to 150 dip or less.
Fig. 2. illustrates a part of a high resolution palm print
image and a low resolution palm print image.
(a) (b)
Fig. 2. Palm print features in (a) a high resolution image and (b) a low
resolution image
In high resolution images, researchers can extract ridges,
singular points and minutia points as features while in low
resolution images, they generally use principal lines, wrinkles
and texture. At the beginning of palm print research, the high-
resolution approach was the focus [7-8] but almost all current
research is focused on the low resolution approach because of
the potential applications. In this paper, we concentrate only
on the low resolution approach since it is the current focus.
For civil and commercial applications, low-resolution palm
print images are more suitable than high-resolution images
because of their smaller file sizes, which results in shorter
computation times during preprocessing and feature
extraction. Therefore, they are useful for many real-time palm
print applications [9].
There are three key issues to be considered in developing
palm print identification system.
1) Palm print Acquisition: How do we obtain a good
quality palm print image in a short time interval, such
as I second? What kind of device is suitable for data
acquisition?
2) Palm print Feature Representation: Which types of
palm print features are suitable for identification? How
to represent different palm print features?
3) Palm print Identification: How do we search for a
queried palm print in a given database and obtain a
response within a limited time?
So far, several companies have developed special scanners
to capture high-resolution palm print images [10, 11]. These
devices can extract many detailed features, including minutiae
points and singular points, for special applications. Although
these platform scanners can meet the requirements of on-line
systems, they are difficult to use in real-time applications
because a few seconds are needed to scan a palm. To achieve
on-line palm print identification in real-time, a special device
is required for fast palm print sampling [9].
III. PROPOSED SYSTEM
In this proposed system, generally consists of four parts:
palm print scanner, preprocessing, feature extraction and
matcher. Palm print scanner is to collect palm print images.
Pre-processing is to setup a coordinate system to align palm
print images and to segment a part of palm print image for
feature extraction.
Palm images are enhanced and pre-processed to get the
region of interest (ROI). Feature extraction is to obtain
effective features from the pre-processed palm print. Finally a
matcher compares two palm print features.
Fig. 3. Stages in palm print identification and verification
A. Pre-processing extraction of Palm print Images
Image is pre-processed to get the region of interest. Pre-
processing includes image enhancement, image binarization,
boundary extraction, cropping of palm print/ ROI. The ROI
size is 64*64 pixels. Sample of ROI is shown in Fig. 4.
Fig. 4.Sample of ROI
B. Normalization of Palm print Images
The extracted palm print images are normalized to have pre-
specified mean and variance. The normalization is used to
reduce the possible imperfections in the image due to sensor

International Journal of Latest Technology in Engineering Management and Applied Science
(IJLTEMAS), vol. 1, issue. X, December 2012
noise and non-uniform illumination. Let the gray level at
(), in a palm print image be represented by . The
mean and variance of image, and , respectively, can be
computed from the gray levels of the pixels. The normalized
image is computed using the pixel-wise operations as
follows:
Where and are the desired values for mean and
variance, respectively. These values are pre-tuned according to
the image characteristics, i.e., . In all our experiments,
the values of and were fixed to 100. Fig. 4. Show a
typical palm print image before and after the normalization.
(a) (b)
Fig. 4.Palm print feature extraction; (a) segmented image, (b) image after
normalization
IV. PALM PRINT IDENTIFICATION AND
VERIFICATION SYSTEM
Palm print identification and verification system using
biometrics is one of the emerging technologies, which
recognizes a person based on the principle lines, wrinkles and
ridges on the surface of the palm. These line structures are
stable and remain unchanged throughout the life of an
individual. More important, no two palm prints from different
individuals are the same, and normally people do not feel
uneasy to have their palm print images taken for testing.
Therefore palm print recognition offers a promising future for
medium-security access control system.
The palm print database is divided into two groups, first
group (G1) consists of 50 persons with each person having 4
palm sample images to train the system, and second group
(G2) contains 50 persons with each person having one palm
image different from the first group images to test the system.
The hand image size is 284*384 pixels. The palm print image
used is 64*64 pixels.
G1 group
P1 = [I1,I2,I3,I4], P2 =[ I1,I2,I3,I4], …………..
P50 = [I1,I2,I3,I4]
In G1 group each hand Pi contains 4 sample image I1-5 .
G2 group
P1 = [5], P2 = [I5],………….P50 = [I5]
In G2 group each hand Pi contains only sample image I6.
Identification and verification is a process of comparing one
image against N images. In the following tests, we setup
registration databases for the number of persons N=50 in four
templates. Similarly for N=50, the registration databases have
200 templates. We also setup a testing database with 200
templates from 50 different palms. None of palm print images
in the testing database are contained in any of the registration
databases. Each of the palm print images in the testing
database is matched with all of the palm print images in the
registration databases to generate incorrect and correct
identification and verification system.
V. CONCLUSIONS
In this paper, we have explored automated palm print
identification and verification. In principle, some features have
been extracted to test the effectiveness of this approach, and
the preliminary results suggest that palm prints can be
effectively applied to identity verification. The technique
works well in the presence of noise in the palm print, because
the features adopted can be obtained from a low-resolution
image. It is believed that this approach can have practical
application to personal identification and verification as a new
biometric technology.
In summary, we conclude that our palm print identification
and verification system can achieve good performance in
terms of speed and accuracy. For further improvement of the
system, we will focus on three issues: 1) to combine the
proposed palm print identification and verification system
with other biometric such as face, finger print for
identification to achieve higher performance, and 2) some
noisy images with cuts and bruises on the hand have to be
collected to test the system.
ACKNOWLEDGMENT
The author is grateful to her family who specifically offered
strong moral and physical support, care and kindness.
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International Journal of Latest Technology in Engineering Management and Applied Science
(IJLTEMAS), vol. 1, issue. X, December 2012
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