Palmprint Authentication System Using Wavelet based … · Palmprint Authentication System Using...

Palmprint Authentication System Using Wavelet based Pseudo Zernike Moments Features

International Journal of The Computer, the Internet and Management Vol. 13 No.2 (May-August, 2005) pp 13-26

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Ying-Han Pang, Dr. Andrew Teoh Beng Jin,

Ass. Prof David Ngo Chek Ling

Faculty of Information Science and Technology Multimedia University, Malaysia Email : [email protected]

Abstract In this paper, a novel method of image

based palmprint matching based on features extracted from wavelet-based pseudo Zernike moments feature descriptor is proposed. Pseudo Zernike moments have additional properties of being more robust to image noise, possessing geometrical invariants property, having a nearly zero value of redundancy measure in a moment set due to its orthogonality property and having a superior image representative capability; while wavelet analysis affords major advantages of performing local analysis (describe image local characteristic) due to its excellent localization property and decomposing image to ease image information interpretation. Therefore, the hybrid wavelet transform and pseudo Zernike moments is able to comprise salient features from both imaging apparatus and achieve better verification rate. Comparison analysis shows that the hybrid wavelet transform and pseudo Zernike moments is able to achieve superior performance than the other well-known moments. Keywords: Biometrics; Palmprint; Feature extraction; Pseudo Zernike moments; Wavelet transform.

1. Introduction Reliability and accuracy in personal

authentication system is a dominant concern to the security world. Traditional methods using passwords, tokens, ID and smart cards will be gradually obsolesced due to their lower reliability and security. The high demand of accurate and reliable authentication solutions stirs up boundless enthusiasm from researchers and industries towards development of biometric identification technology. Nowadays, biometric technology has emerged as a cutting edge technology and is launched in a large space in the applications of physical access control, airport security, identity authentication, data security, forensics and others. Researches on the issues of fingerprint identification, iris verification, facial recognition and speech recognition have been carried out extensively in both academia and industry. Palmprint identification and verification is an innovative endeavor especially in academic research field. Palmprint provides abundant and stable attributes, such as principal lines, wrinkles, minutiae, delta points, etc., to palmprint recognition system. Palmprint offers advantages: non-intrusive, low-resolution imaging, user-friendly, moderate-price capture devices, stable and distinct features [2] [6].

mailto:[email protected]

Ying-Han Pang , Andrew Teoh Beng Jin, David Ngo Chek Ling

Moments feature extractor is a renowned object and shape feature extraction algorithm employed in binary character and signature recognition due to their orthogonality property and translation, rotation and scaling invariance. However, the report of moments’ discriminative competence in natural and grey scale image, particularly in the biometric application, is still rarely found. On the other hand, wavelet transform is a popular mathematical tool and widely implemented in decomposing image into a multiresolution representation to ease image information interpretation.

Wavelet transform offers high temporal localization for high frequencies while offering good frequency resolution for low frequencies. Therefore, wavelet analysis has been universally utilized to extract local characteristics from still images due to its local extent [3]. If an orthonormal wavelet basis, say daubechies or symlets, was chosen, the coefficients computed are independent and obtain a set of distinct features of the original signal. Besides, wavelet transform decomposes image into a multiresolution representation, which grants a structural configuration for analyzing the image information. Pseudo Zernike moments are able to depict image features independently and thus obtain minimum information redundancy in a moment set due to their orthogonality property. In addition, pseudo Zernike moments also retain salient features of robustness with respect to noise and grasp plentiful image information content. Consequently, the integrated wavelet transform and pseudo Zernike moments, denoted hereinafter as WTPZM, is able to incorporate the advantages from these both means.

Our designed palmprint verification system is generally composed of four stages. In the first stage, localization of palmprint region is implemented. Furthermore, the localized palmprint image (region of interest) is enhanced by means of local histogram

equalization. The second stage involves decomposition of palmprint image by applying wavelet transform. The wavelet decomposition reduces the resolutions of the subband images, and thus trims down the computational complexity. The third stage engages wavelet based feature extraction by implementing pseudo Zernike moments as feature descriptors. For the dissimilarity matching, a simple Euclidean distance metric is selected in the forth stage.

This paper is organized under eight sections: section 2 presents palmprint image preprocessing, while section 3 and section 4 illustrate a brief review of wavelet transform and pseudo Zernike moments, respectively. Palmprint authentication system using integrated framework of wavelet transform and pseudo Zernike moments (WTPZM) is described in section 5. Section 6 shows the experimental results and discussion about the results is having in this section, too. Concluding remarks is followed in section 7.

2. Palmprint Image Preprocessing

Normally, palmprint images captured in

the image acquisition stage are gray-scale and subject to noise. Moreover, these captured images not only do contain region of interest (palmprint), but also contain region of not-interest (fingers, image background, etc.). Therefore, image preprocessing is a necessary and crucial step in palmprint verification system before processing the image. The preprocessing of our system is composed of two steps:

• Palmprint Image Localization

It is commonly known that accurate and precise palmprint localization results more robust and accurate palmprint feature extraction. In this paper, the region of interest, abbreviated hereinafter as ROI, is defined in a square shape after the correction of orientation. Then the ROI is converted to a fixed size (150 x 150 pixel matrix) so that

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all of the palmprints conform to a same size. The ROI in this paper is defined in square shape (see Figure 1(a)).

The basic steps to extract the region of interest (ROI) are as follows:

1. Use the eight-neighborhoods-pixels border tracing algorithm to find the valleys v1, v2, and v3 of the fingers [4].

2. The two valleys beside the middle finger, v1, v2, are connected to form a reference line.

3. The reference line is extended to touch the right-edge of the hand.

4. The intersection point obtained from step 3 is used to find the midpoint, m1.

5. Steps 2 to 4 are repeated to find the other midpoint, m2, by using the valleys v2, v3.

6. The two midpoints, m1 and m2, are

connected to form the base line to obtain the ROI.

7. By using right-angle coordination system, the square outline of the ROI can be obtained.

After obtaining the outline of the ROI,

the image is rotated so that the vertical edges of the ROI are perpendicular to the x-axis, as shown in Figure 1(b). The localized palmprint is depicted in Figure 1(c)

• Palmprint Image Enhancement

Low contrast and non-uniform illumination of palmprint image are other main factors that result in inferior performance. Local histogram equalization is implemented to deal with this problem. The enhanced localized palmprint is shown in Figure 1(d).

Reference Line Extended Line Valley

X Midpoint Intersection Point v2

v1

v3

m1 X

m2 X

Region of Interest

(a) (b)

(c) (d)

Figure 1. The palmprint (a) Region of interest (ROI) of the palmprint (b) Rotated palmprint (c) Palmprint before enhancement (d) Palmprint after enhancement


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Ying-Han Pang , Andrew Teoh Beng Jin, David Ngo Chek Ling 3. Wavelet Transform

Basically, wavelet transform represents

image as a sum of wavelets on different resolution levels. The key upper hand of the wavelet transform is it offers high temporal localization for high frequencies while attempts good frequency resolution for low frequencies. Thus, wavelet transform is able to capture local characteristics of image and in this way we have a localized view of the image/signal’s behavior.

3.1.Review of Discrete Wavelet Transform

The wavelet decomposition of a signal

f(x) can be obtained by a convolution of signal with a family of real orthonormal

basis, )(xabψ

)()(

)(),))(((

2

21

ℜ∈

⎥⎦⎤

⎢⎣⎡ −

= ∫ℜ

Lxf

dxa

bxxfabaxfW ψψ

(1) where a, and are the dilation

parameter and the translation parameter

respectively. The basis function

ℜ∈b 0≠a

)(xabψ is

obtained through translation and dilation of a

kernel function )(xψ known as mother

wavelet [3] as defined below:

)2(2)( 2/, bxx aaba −= −− ψψ (2)

The mother wavelet )(xψ can be

constructed from a scaling function )(xφ . The scaling function )(xφ satisfies the following two-scale difference equation

∑ −=

nnxnhx )2()(2)( φφ (3)

where h(n) is the impulse response of a discrete filter which has to meet several conditions for the set of basis wavelet functions to be orthonormal and unique [3]. The scaling function )(xφ is related to the mother wavelet )(xψ via

∑ −=n

nxngx )2()(2)( φψ (4)

The coefficients of the filter g(n) are

conveniently extracted from filter h(n) from the following relation

g(n) = (-1)nh(1-n) (5) The discrete filters h(n) and g(n) are the

quadrature mirror filters (QMF), and can be used to implement a wavelet transform instead of explicitly using a wavelet function.

For 2D signal such as image, there exists an algorithm similar to the one-dimensional case for two dimensional wavelets and scaling functions obtained from one-dimensional ones by tensiorial product. This kind of two-dimensional wavelet transform leads to a decomposition of approximation coefficients at level j-1 in four components: the approximations at level j, and the details in three orientations (horizontal, vertical and diagonal)

),(]]*[*[),( 2,11,21 nmLHHnmL jyxj ↓↓−= (6)

),(]]*[*[),( 2,11,21 nmLGHnmD jyxjvervital ↓↓−=

(7)

),(]]*[*[),( 2,11,21 nmLHGnmD jyxljhorizonta ↓↓−=

(8)

),(]]*[*[),( 2,11,21 nmLGGnmD jyxjdiagonal ↓↓−=

(9)

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where * denotes the convolution

operator, subsampling along the columns (rows), H and G are a low pass and bandpass filter, respectively. The decomposition algorithm is illustrated in Figure 2.

)2,1(1,2 ↓↓

3.2. Palmprint Images in Wavelet Domain

In this paper, discrete wavelet transform

is used to decompose the palmprint image into a multiresolution representation in order to keep the least coefficients possible without losing useful image information. The

multiresolution character of the wavelet decomposition leads to superior energy compaction (high image information content) and compact constitution of decomposed image. Figure 3(a) demonstrates the decomposition process by applying two-dimensional wavelet transform of a palmprint image in level 1 and Figure 3(b) depicts two levels wavelet decomposition by applying wavelet transform on the low-frequency band sequentially.

Figure

Internatio

Figure 2. Decomposition of two-dimensional signal using conjugate filter of H

3. Palmprint image in different wavelet subbands (a) 1-level wavelet decomposition

(b) 3-level wavelet decomposition

nal Journal of The Computer, the Internet and Management Vol. 13 No.2 (May-August, 2005) pp 13-26

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For many signals, including images, the low-frequency content is the most important part. It is what gives the signal its identity. The high-frequency content, on the other hand, imparts flavor or nuance. The approximation subbands, Lj, are the high-scale, low-frequency components of the signal. However, the detail subbands, Dj, are the low-scale and high-frequency components. There are three main types of detail subbands:

Djhorizanl corresponds to the high-

frequency component in the horizontal direction (horizontal edges), Djvertical in the vertica edges and Djdiagonal in both horizontal and vertical directions.

For palmprint images, the high-

frequency subbands from wavelet decomposition are not suitable to represent palmprint due to their sensitivity to variations in the image. Their high pass features tend to enhance the edges detail and thus susceptible to noise and shape distortion. Nevertheless, the approximation subbands corresponding to the low-frequency components are actually a glossy version of original image. These low- frequency subbands not only preserve the local characteristics of the image, but also insensitive to the image noise and minor distortion.

4. Pseudo Zernike Moments

In order to design an accurate palmprint

authentication system, feature extraction algorithm comes to play an important role. Pseudo Zernike moments which offers additional properties of being more robust in the presence of image noise, having a zero value of redundancy measure in a moment set and having a higher degree of information content [1] is selected as a feature descriptor in this study.

4.1. Review of Pseudo Zernike Moments The kernel of pseudo Zernike moments

is the set of orthogonal pseudo Zernike polynomials defined over the polar coordinates inside a unit circle. The two-dimensional pseudo Zernike moments of order p with repetition q of an image intensity function ),( θrf are defined as [1]:

θθπ

π

rdrdrVpPZ pqpq ),(1 2

0

1

0∫ ∫

+= (10)

where Zernike polynomials ),( θrVpq are

defined as: ∧

−= θθ jqpqpq erRrV )(),( , 1−=

∧

j (11)

and

22 yxr += , ⎟⎠⎞

⎜⎝⎛= −

xy1tanθ ,

-1<x,y<1 The real-valued radial polynomials is

defined as:

sp

sqp

spq r

sqpsqpssprR −

−

= −−−++−+

−= ∑ )!()!1(!)!12()1()(

0

(12) and 0,0 ≥≤≤ ppq Since it is easier to work with real

functions, is often split into its real

and imaginary parts, as given

below:

,pqPZ

,cpqPZ s

pqPZ

θθθπ

π

rdrdrfqrRpPZ pqcpq ),()cos()()1(2 2

0

1

0∫ ∫

+=

(13)

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θθθπ

π

rdrdrfqrRpPZ pqspq ),()sin()()1(2 2

0

1

0∫ ∫

+=

(14) where >0 qp ,0≥

5. Integrated Wavelet Transform and

Pseudo Zernike Moments (WTPZM) Palmprint Authentication System The integrated framework of wavelet

transform and pseudo Zernike moments (WTPZM) outcomes a lower-resolution image with minor distortion and noise insusceptibility. Reduction of image resolution helps lighten the pseudo Zernike moments feature generation load and speed up the computational time. Original palmprint image with fixed size of 150x150 pixels is input into wavelet transformation process for decomposition purpose. Thus, a

low-dimension multiresolution representa-tion is produced to ease image information interpretation. Then, pseudo Zernike moments as feature descriptor is applied on the wavelet based features to obtain higher discriminatory WTPZM features. In this paper, only three levels of wavelet decomposition are being performed because too high level of decomposition produces image with too course resolution in where it is not able to provide leading information to represent the original image.

There are two phases in our system: enrollment and verification. Both phases comprise two sub-modules: preprocessing for palmprint localization and enhancement and feature extraction for wavelet based moment features extraction. However, verification phase consists of an additional sub-module, classification, for calculating the dissimilarity matching of the palmprint. Figure 4 shows the palmprint authentication system block diagram.

International Jo

Figure 4. Block diagram of palmprint verification system

urnal of The Computer, the Internet and Management Vol. 13 No.2 (May-August, 2005) pp 13-26

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At the enrollment stage, a set of the template images represented by wavelet based moment features is labeled and stored into a database. At the verification stage, an input image is converted into a set of wavelet-moment features, and then is matched with the claimant’s palmprint image stored in the database to gain the dissimilarity measure by computing Euclidean distance metric. We used this distance metric instead of more complex classification algorithm (e.g. neural network) because we were just focusing on the feature extracting rather than the classification. Finally, the dissimilarity measure is compared to a predefined threshold to determine whether a claimant should be accepted. If the dissimilarity measure below the predefined threshold value, the palmprint input is verified possessing the same identity as the claimed identity template and the claimant is accepted.

6. Experimental Results and Discussion

6.1. Data Collection Experiments are conducted by using a

set of database which possessing 50 different palmprint classes, with six samples for each class. This makes up a total of 300 experimental palmprint samples. For each palmprint pattern, the first, third and fifth sample were used for testing and the rest for

training. These six images were acquired under different conditions for each palmprint class. They vary in position, rotation and scale in the acquisition stage. The region of interest is cropped, converted to 16-bit grayscale and resized into 150 x 150 pixel matrix so that all of the palmprints conform to a same size. A few palmprint samples are shown in Figure 5. In the classification phase, there are a total of matching genuine scores of 3x50=150 and 49x50x3=7350 for imposter attempts using Euclidean distance metric.

6.2. Determination of Optimum Moments

An experiment was conducted using

different settings of feature vectors based on the order of pseudo Zernike moments for the purpose of determining the moments that optimally describe palmprint features. The result of recognition rate measured by Euclidean distance is shown in Figure 6. The figure shows that moments of order 9, which obtain the highest recognition rate, possesses the best feature vectors that optimally describe the palmprint. Therefore, moments of order 9 are selected for the continuous experiments in this study. Furthermore, the result also illustrates that the higher the order of the pseudo Zernike moments, the higher recognition rate of the system. Higher order moments capture finer details about the image.

Figure 5. A few palmprint samples that used in the experiments

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Figure 6. Plot of recognition rate versus moment orders

6.3. Experimental Results

The following experiment is conducted

by integrating pseudo Zernike moments of order 9 with various wavelet filters. Three types of wavelet filters have been tested in this experiment, namely the Haar, Daubechies and Symmlet filter. The verification performances of these filters are shown in Table 1.

From Table 1, wavelet basis with

Symmlet orthonormal wavelet filter order 8, level 2 performs the best verification rate with FAR=4.28%, FRR=4.32% and TSR=95.72%. We can observe that the performance of most of the wavelet filters with decomposition level 3 is poorer than the result provided by decomposition level 1 and 2. This indicates that the excessive down sampling process gets rid of the line feature structures of the coarser images; and this downgrades the discriminatory power of the WTPZM features.

An experiment has also been conducted

by applying the palmprint images on the various subbands of wavelet. The purpose of the experiment is to justify that the high frequency subbands are not favorable to be

selected as the WTPZM features. By using the chosen wavelet basis, which is Symmlet orthonormal wavelet filter order 8 with decomposition level 2, the verification rate of four subbands at level 2 is indicated in Table 2. The table reveals that approximation subband, L2, gives a prime result, yet the other frequency subbands perform unsatisfying performance rate. This shows that high frequency subbands are inadequate feature descriptors because none of them can flawlessly describe the print structure of the palmprint image. High frequency subbands are also sensitive to image variation due to its high pass feature that tends to comprise noise. This result justifies the proper employ of second level approximation scale, L2 in WTPZM representation. There is a direct relation in between the ability of a wavelet subband to preserve the energy in the palmprint images and the discriminatory power of the subband. From Figure 7, we can notice that the approximation subbands of all wavelets are superior in the energy preservation with nearly 100% of energy content. In contrary, the other high frequency subbands, H vertical, H horizontal and H diagonal, are unable to preserve the energy (less than 1.0% energy content).


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Table 1. Comparative result of verification rate among various wavelet filters in WTPZM schme

No. Filter Decomposition Level FAR(%) FRR(%) TSR(%) 1 5.16 5.00 94.84 2 5.27 5.23 94.75

1

Haar

3 5.22 5.27 94.78 1 5.26 5.23 94.74 Daubechies 2 2 5.37 5.45 94.62

2

3 5.27 5.23 94.72 1 5.25 5.23 94.75 3 Daubechies 3 2 4.79 4.77 95.21 3 6.09 6.14 93.91 1 5.31 5.45 94.70 4 Daubechies 4 2 5.12 5.23 94.88 3 4.43 4.55 95.56 1 5.23 5.00 94.77 5 Daubechies 5 2 5.17 5.23 94.83 3 4.94 5.00 95.06 1 5.28 5.45 94.71 6 Daubechies 6 2 4.76 4.77 95.22 3 5.73 5.91 94.26 1 4.98 5.00 95.02 7 Daubechies 7 2 4.50 4.77 95.49 3 6.81 6.82 93.19 1 5.11 5.23 94.89 8 Daubechies 8 2 4.59 4.55 95.42 3 6.09 5.91 93.91 1 5.11 5.23 94.89 9 Symmlet 4 2 4.83 4.77 95.17 3 4.62 4.55 95.38 1 4.90 4.77 95.10 10 Symmlet 5 2 4.56 4.55 95.44 3 4.68 4.77 95.31 1 4.84 4.77 95.16 11 Symmlet 6 2 4.56 4.55 95.44 3 5.27 5.45 94.73 1 4.85 5.00 95.15 12 Symmlet 7 2 4.83 4.77 95.18 3 5.95 5.91 94.05 1 4.73 4.77 95.27 13 Symmlet 8 2 4.28 4.32 95.72 3 5.99 6.14 94.01 1 4.72 4.77 95.28 14 Symmlet 9 2 4.63 4.77 95.37 3 5.12 5.68 94.48 1 4.74 4.77 95.26 15 Symmlet 10 2 4.55 4.55 95.45 3 5.80 5.91 94.20

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Figure 7. Energy content of wavelet subbands

A comparison analysis between the

proposed method and other approaches, such as solely pseudo Zernike moments (PZM), Zernike moments, Geometric moments and WPCA method based on Connie et al [7] work, is presented. Orthogonal moments- pseudo Zernike moments and Zernike moments, have additional properties of being more robust in the presence of image noise, having a nearly zero value of redundancy measure in a moment set and having a higher degree of information content. On the other hand, the dominant advantage of Geometric moments is image coordinate transformations can be easily expressed and analyzed in terms of the corresponding transformation in the moment space. Besides that, their computations on image can be easily performed and implemented compared

to other moments [5]. However, Geometric moments do not possess orthogonality property. This might cause high information redundancy and result in poor feature representation capability. For the WPCA approach, the decomposed images, via wavelet transformation, are fed into Principal Component Analysis (PCA) computation. PCA is able to extract important palmprint features from the palmprint, and locate the similar print structures in a narrow image space. Then, PCA generates a new set of feature vectors, called principal components, where each principal component is a linear combination of the original vector [7].

Table 3 below shows the comparative

results in between the proposed method with the approaches discussed above. Note that


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Ying-Han Pang , Andrew Teoh Beng Jin, David Ngo Chek Ling only the best verification rate achieved reported in [7] is taken for the comparison. From Table 3, our proposed method signifies the better verification rate among the other approaches. Wavelet transformation implemented in WTPZM helps reduce noise while preserving the local edges well which aids to capture features that insensitive to small distortion. Thus, WTPZM obtains higher total success rate than solely PZM. Figure 8 depicts decrement of EER for WTPZM scheme compared to PZM scheme. The lower and further left of the curve on the graph, the better the performance it is. Thus Figure 8 exhibits the robustness of WTPZM scheme in verification task. Besides, implementation of wavelet transform in WTPZM scheme for decomposing the image helps reduce the pseudo Zernike moments

computational load and speed up the feature generation computational time. The time elapsed for generating features in WTPZM and PZM scheme is shown in Table 4. Feature generation computational time in WTPZM scheme is only 11.63% of that in the PZM scheme. Besides, Figure 9 demonstrates noise insusceptibility capability of the WTPZM compared to PZM profile. WPCA, which also goes through wavelet transformation process, exhibits slightly inferiors compared to the proposed method. This reveals that pseudo Zernike moments descriptor is able to define statistical and geometrical features containing line structural information about palmprint better than WPCA.

Figure 8. Plot of Receiver Operating Curve (ROC) for WTPZM and PZM scheme

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Figure 9. Plot of recognition rate versus noise for WTPZM and PZM schemes

7. Concluding Remarks The objective of this work is to

investigate the integration of wavelet transform and pseudo Zernike moments as feature extractor in the positive palmprint authentication system and to achieve superior performance than the other feature extraction algorithms as discussed in section 6. In this paper, the relationship between the order moments of pseudo Zernike moments and the accuracy rate is shown, too. Higher order moments of pseudo Zernike moments comprise more prominent information about

palmprint image and are effective in defining palmprint features. In our system, before implementing feature extraction by means of pseudo Zernike moments, the localized palmprint image is first decomposed into a lower-resolution through wavelet transformation. This transformation not only produces multiresolution representation that alleviates the computational hard work, but also generates noise and minor distortion insusceptible features. The overall verification rate of the proposed method is 95.72%.


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Ying-Han Pang , Andrew Teoh Beng Jin, David Ngo Chek Ling References 1. C.H. Teh and R.T. Chin, “On Image

Analysis by the Methods of Moments”, IEEE Trans. Pattern Anal. Machine Intell., vol. 10, July 1988, pp. 496-512.

2. J. You, W. Li and D. Zhang, “Hierarchical Palmprint Identification via Multiple Feature Extraction”, Pattern Recognition, vol. 35, 2002, pp. 847-859.

3. Mallat S., A Wavelet Tour of Signal Processing. San Diego: Academic Press, 1998.

4. M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis and Machine Vision, PWS publisher, 1999.

5. R. Mukundan and K.R. Ramakrishnan, “Moment Functions in Image Analysis – Theory and Applications”, World Scientific Publishing, 1998.

6. Shu Wei and D. Zhang, “Palmprint Verification: an Implementation of Biometric Technology”, Proceedings of ICRP’98, Brisbane, Queensland, Australia, Aug. 1998, pp. 219-221.

7. Tee Connie, Michael Goh, Andrew Teoh, David Ngo, “An Automated Biometric Palmprint Verification System,” 3rd Int. Symp. On Communications & Info. Tech. (ISCIT2003), 2002, vol. 2, pp. 714-719.

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