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Neurocomputing 71 (2008) 3032–3036

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Letters

Palmprint recognition using Gabor feature-based (2D)2PCA

Xin Pana,b,�, Qiu-Qi Ruana

aInstitute of Information Science, Beijing Jiaotong University, Beijing 100044, PR ChinabCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Huhhot 010018, PR China

Received 25 November 2006; received in revised form 12 August 2007; accepted 12 December 2007

Communicated by L.C. Jain

Available online 7 February 2008

Abstract

In this paper, we propose a novel approach of Gabor feature-based (2D)2PCA (GB(2D)2PCA) for palmprint recognition. Three main

steps are involved in the proposed GB(2D)2PCA: (i) Gabor features of different scales and orientations are extracted by the convolution

of Gabor filter bank and the original gray images; (ii) (2D)2PCA is then applied for dimensionality reduction of the feature space in both

row and column directions; and (iii) Euclidean distance and the nearest neighbor classifier are finally used for classification. The method

is not only robust to illumination and rotation, but also efficient in feature matching. Experimental results demonstrate the effectiveness

of our proposed GB(2D)2PCA in both accuracy and speed.

r 2008 Elsevier B.V. All rights reserved.

Keywords: Palmprint recognition; Feature extraction; Gabor filter bank; (2D)2PCA

1. Introduction

Palmprint recognition has attracted wide attention fromresearchers as a new biometrics authentication technology.The palmprint, the unique inner surface of hand, contains anumber of distinctive features such as principal lines,wrinkles, ridges and minutiae. Compared with othertechnologies, the advantages of palmprint recognition arelow resolution, low cost, non-intrusiveness and stablestructure features [2].

Studies on palmprint recognition have focused on thefeature extraction of line, texture, statistics and multiplerepresentations. Line-based recognition is often seen inearlier works on off-line palmprint images with highresolution (up to 500 dpi) [14]. But for online palmprintimages, the line feature extraction is difficult for the low-resolution images (less than 100 dpi). Texture-basedfeatures can be obtained by transform and wavelet [8].2D Gabor filter is a common texture-based method for

e front matter r 2008 Elsevier B.V. All rights reserved.

ucom.2007.12.030

ing author at: Institute of Information Science, Beijing

rsity, Beijing 100044, PR China. Tel.: +86 13466717014.

esses: pxffyfx@126.com (X. Pan),

.njtu.edu.cn (Q.-Q. Ruan).

palmrprint recognition [13,5], which provides high-recognition accuracy at the cost of speed and memory.Statistical approaches, such as Principal ComponentAnalysis (PCA), linear discriminative analysis (LDA) andtwo-dimensional PCA (2DPCA), obtain eigenpalms, fish-erpalms and some other features in palmprint recognition[9,11,7]. The fusion of different features and the integrationof multiple approaches are also used in the late literature[4,6], which lead to relatively satisfying results.PCA is a representative statistical method, which can

effectively reduce the dimension of image space as a whole,whereas 2DPCA treats the images directly with a betterrecognition performance and time consumption [12].However, the main disadvantage of 2DPCA is that toomany coefficients are needed for image representation.(2D)2PCA, an improved 2DPCA algorithm, overcomes theproblem by projecting the images onto row and columndirections simultaneously [15].Nevertheless, either PCA or its derived methods are

sensitive to variations caused by illumination and rotation.But some works [13,5] showed that Gabor filters canprovide robust features against varying brightness andcontrast of images. In their method, Gabor features,derived from the convolution of a Gabor filter and

ARTICLE IN PRESSX. Pan, Q.-Q. Ruan / Neurocomputing 71 (2008) 3032–3036 3033

palmprint images, were represented and matched byhamming code and hamming distance, respectively. How-ever, the procedure for feature coding and matching bypixels requires too much time and memory. Moreover, toextract more local features from the original images, aseries of Gabor filters with various scales and orientations(called Gabor filter bank) are needed in most casesof biometrics. Eventually this will enlarge the featuredimension by time and make feature matching beyondimplementation.

Taking together both procedures, we develop a newalgorithm for palmprint recognition, Gabor feature-based(2D)2PCA (GB(2D)2PCA). The proposed GB(2D)2PCAconsists of three steps: initially, Gabor feature matrix ofdifferent scales and orientations are extracted by theconvolution of Gabor filter bank and original images.All the Gabor feature matrices of training samplescompose Gabor feature space. Subsequently, (2D)2PCAreduce the dimension of Gabor feature space in both rowand column directions resulting in fewer coefficients forfeature matching. Finally, Euclidean distance and thenearest neighbor classifier are used for feature matchingand classification.

The rest of this paper is organized as follows: Sections 2and 3 present our algorithm in sequence, the formerintroduces Gabor filter bank for feature extraction, and thelatter explains (2D)2PCA for dimensionality reduction offeature space. Experimental results are given in Section 4.Section 5 highlights the conclusions.

2. Gabor filter bank for feature extraction

2D Gabor has the following general form [1]:

Gðx; y; y; u;sÞ ¼1

2ps2exp �

x2 þ y2

2s2

� �

� expf2piðux cos yþ uy sin yÞg (1)

where i ¼ffiffiffiffiffiffiffi�1p

, u is the frequency of the sinusoidal wave, ycontrols the orientation of the function and s is thestandard deviation of the Gaussian envelope. Kong et al.[5] designed 12 Gabor filter combinations of different u, yand s. By comparing the receiver operating characteristic(ROC) curves, y ¼ 0, u ¼ 0.0916 and s ¼ 5.6179 is thefavorite group for 128� 128 palmprint images.

Taking into account the above result, and in order toextract more effective features on various orientations andscales, we design a Gabor filter bank in our approach. Fivescales are chosen for different local features

u ¼ 0:2592. ffiffiffi

2p

v; v ¼ 0; 1; . . . ; 4 (2)

(when v ¼ 3, u ¼ 0.0916). Considering the appearance ofthe main principles in palmprint images, we choose six

orientations:

yk ¼pðk � 1Þ

6; k ¼ 1; 2; . . . ; 6. (3)

Thus 30 Gabor filters are selected for feature extraction.Suppose that there are N 128� 128 training palmprint

images, denoted by matrices Ai (i ¼ 1,2,y,N). Theconvolution of the Gabor filter bank and image Ai yieldsGabor feature matrices Hi(v, k) (v ¼ 0,y,4; k ¼ 1,y,6).For simplicity, we downsample the Gabor feature by oneper four rows and columns, i.e. the downsampling rater ¼ 16. By concatenating all the 32� 32 downsampledGabor feature matrices Oi(v, k) (v ¼ 0,y,4; k ¼ 1,y,6) inthe column direction, the Gabor feature matrix Xi of imageAi can be represented as

X i ¼ fOið0; 1Þ0;Oið0; 2Þ

0; . . . ;Oið4; 6Þ0g0 (4)

and Xi has 32� 30 ¼ 960 rows and 32 columns. The Gaborfeature space X is constructed by all the Gabor featurematrices of training samples in the row direction X ¼ {X1,X2,y, XN}, the dimension of which is 960� 32N.

3. (2D)2PCA for dimensionality reduction of Gabor feature

space

In 2DPCA, the covariance matrix G can be evaluated by

G ¼1

N

XN

i¼1

ðX i � X ÞTðX i � X Þ (5)

where

X ¼1

N

Xi

X i. (6)

Since the size of Xi is 960� 32, G has a dimension of32� 32. The orthonormal eigenvectors of G correspondingto the d largest optimal value is proved to be optimalprojection matrix [3]

Ropt ¼ ½r1; . . . ; rd �. (7)

The value of d can be determined by the ratio of the sumof chosen d largest eigenvalues to all.Similarly, the optimal projection in the column direction

Copt ¼ ½c1; . . . ; cq� is obtained by the transposed spacematrix. (2D)2PCA treats the feature matrix Xi both in rowand column directions simultaneously by projecting theoriginal feature matrix onto Copt and Ropt [15]

Y i ¼ CToptX iRopt. (8)

Here Yi is a coefficient matrix to represent palmprint imageAi. Because Xi is composed of 30 32� 32 matrices in thecolumn direction, Yi can be obtained by projecting all these32� 32 blocks in Xi onto Copt and Ropt, respectively, andarranging them in the same order. Suppose the dimensionsof Copt and Ropt are 32� 6 and 32� 12, the ultimate

ARTICLE IN PRESSX. Pan, Q.-Q. Ruan / Neurocomputing 71 (2008) 3032–30363034

dimension of a Gabor feature vector is reduced from32� 30� 32 ¼ 30,720 to 6� 30� 12 ¼ 2160.

Euclidean distance and the nearest neighbor classifier areadopted in our experiments for classification, without

Fig. 1. Original image and experimental image: (a) original hand image;

(b) palmprint image.

Fig. 2. Palmprint examples (images in same column from one palm).

Table 1

Comparison of correct recognition rates (%) on different methods

Method Training sample number per class

5 4 3

PCA 95.00 (53) 94.00 (40) 8

2DPCA 95.00 (128� 10) 94.25 (128� 13) 9

(2D)2PCA 96.00 (10� 8) 95.50 (9� 9) 9

GBPCA 98.00 (78) 97.00 (79) 9

GB2DPCA 99.00 (32� 30� 12) 98.50 (32� 30� 9) 9

GB(2D)2PCA 99.00 (6� 30� 12) 98.50 (7� 30� 12) 9

losing generality. Due to fewer coefficients for comparison,the testing time will be shortened considerately.

4. Experiments

4.1. Palmprint database

We collected 800 left-hand images of 595� 790 pixels of75 dpi resolution from 80 subjects by a digital scanner inour lab at an interval of 3 month. The central 128� 128pixels of each hand image extracted by the preprocessingmethod [10] constitute a palmprint database (Fig. 1). Fiveimages of each subject are randomly chosen for training,and the remaining five images are used for testing. So thetraining set and testing set contains 400 images, respec-tively. Fig. 2 exemplifies some palmprint samples in thepalmprint database. All the experiments are executed on acomputer system of PIV 2.67GHz and 256MB RAM withMatlab 6.1.

4.2. Experimental results

In this experiment, the correct recognition rate andtesting time of the proposed GB(2D)2PCA are investigated.The parameter setting of Gabor filter bank is depicted inpart 2, and the downsampling rate r remains 16 for all theGabor feature-based algorithms.Table 1 presents the comparison of correct recognition

rate using PCA, 2DPCA and (2D)2PCA on raw images andGabor features. GBPCA and GB2DPCA (both Gaborfeature based) (Table 1) denote implementation of PCAand 2DPCA on Gabor features. The feature dimensions ofdifferent methods are dynamically determined by ratio ofthe sum of chosen largest eigenvalues to all (about 93%),which are listed in the parentheses right to the correspond-ing recognition rates. As can be seen, both GB(2D)2PCAand GB2DPCA achieve a recognition rate of 99% whenthe sample number per class is 5, 3% higher than(2D)2PCA. With the decrease of training sample number,GB(2D)2PCA prevails over other methods. For example,when the training sample number per class is 1,GB(2D)2PCA outperforms GB2DPCA for 1%.To make further comparison between the performance

of GB(2D)2PCA and GB2DPCA, their ROC curves are

2 1

9.50 (35) 84.25 (35) 70.25 (30)

0.00 (128� 14) 85.50 (128� 11) 78.50 (128� 13)

1.00 (10� 10) 88.00 (13� 11) 84.00 (14� 12)

5.00 (66) 92.00 (52) 80.75 (32)

6.00 (32� 30� 9) 92.50 (32� 30� 11) 89.00 (32� 30� 10)

6.00 (6� 30� 13) 94.50 (11� 30� 12) 90.00 (8� 30� 12)

ARTICLE IN PRESS

Fig. 3. Comparative ROC curves.

Table 2

Comparison of testing time and final dimension

Method Testing time (s) Dimension

GB2DPCA 12.45 32� 30� 12

GB(2D)2PCA 5.8 6� 30� 12

X. Pan, Q.-Q. Ruan / Neurocomputing 71 (2008) 3032–3036 3035

plotted in Fig. 3. Each of the palmprints in the testing set ismatched with all images in the training set. Since each classhas 5 samples in the training set, there are 5 correct and 395incorrect matches for each sample in the testing set. Totallythere are 2,000 correct and 158,000 incorrect matches. Ifthe matching distance between two images is smaller thanthe threshold in correct matches, the match is a genuineacceptance. Fig. 3 shows that genuine acceptance rate(GAR) of GB(2D)2PCA is higher than that of GB2DPCAat the same false acceptance rate (FAR).

The testing time and final dimension of the above twomethods are compared in Table 2. The table shows that thetesting time required by GB(2D)2PCA is only half thanthat of GB2DPCA. The less testing time consumed byGB(2D)2PCA is due to smaller dimension of Gabor featurevector.

5. Conclusion

This paper reports a novel GB(2D)2PCA method forpalmprint recognition. The novelty of the GB(2D)2PCAcomes from implementation of(2D)2PCA on an augmentedGabor feature vector first derived from a Gabor filter bank,not a single Gabor filter, for palmprint images. TheGB(2D)2PCA method, which is more robust to variations

of illumination and rotation, uses Gabor feature vectorof five scales and six orientations as an input of (2D)2

PCA instead of raw palmprint images. Meanwhile,GB(2D)2PCA can reduce the augmented Gabor featurevector in two directions , and hence fewer coefficientsare required for image representation and recognition.Using this method, we achieve a higher correct recognitionrate, a better ROC performance and less testing time.In summary, for palmprint recognition, the proposedGB(2D)2PCA is effective in both recognition accuracyand speed.

Acknowledgments

The authors are grateful to Dr. Lucheng Cao and theanonymous reviewers for their constructive comments andadvices. This work is supported partly by the NationalNatural Science Foundation of China under Grant Nos.60472033, 60672062, and the National Grand Fundamen-tal Research 973 Program of China under Grant No.2004CB318005.

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Xin Pan received her B.S. and M.S in Xi’an

Institute of Technology and Inner Mongolia

Agricultural University in 1997 and 2000. She

has worked in College of Computer and Infor-

mation Engineering, Inner Mongolia Agricultur-

al University since then. Now she is pursuing her

Ph.D. degree at the Institute of Information

Science, Beijing Jiaotong University. Her re-

search interests include image processing, pattern

recognition, etc.

Qiu-Qi Ruan was born in 1944. He received the

B.S. and M.S. degrees from Northern Jiaotong

University, China in 1969 and 1981, respectively.

From January 1987 to May 1990, he was a

visiting scholar in the University of Pittsburgh,

and the University of Cincinnati. Subsequently,

he has been a visiting professor in USA for

several times. He has published 2 books and more

than 100 papers, and achieved a national patent.

Now he is a professor, doctorate supervisor at the

Institute of Information Science, Beijing Jiaotong University. He is a

senior member of IEEE. His main research interests include digital signal

processing, computer vision, pattern recognition and virtual reality.