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 1rajputky9@rediffmail.com

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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