Palmprint authentication using fusion of wavelet and contourlet features

Transcription

1 SECURITY AND COMMUNICATION NETWORKS Security Comm. Networks 2011; 4: Published online 22 October 2010 in Wiley Online Library (wileyonlinelibrary.com)..234 SPECIAL ISSUE PAPER Palmprint authentication using fusion of wavelet and contourlet features S. M. Prasad 1, V. K. Govindan 1 and P. S. Sathidevi 2 1 Department of CSE, National Institute of Technology Calicut, Kerala , India 2 Department of ECE, National Institute of Technology Calicut, Kerala , India ABSTRACT Low resolution palmprint images consist of discriminative multisized and multidirectional principal lines and wrinkles. Intuitively, discrete wavelet transform (DWT) is a good choice to extract such patterns due to its space-frequency localization, multiresolution analysis (MRA) capability, and computational efficiency. However, most of the DWT-based palmprint recognition systems fail to report low equal error rate (EER) due to inherent limitations of DWT and shift-rotational variations in the intraclass palmprint images. This paper proposes the techniques for shift and rotation invariant feature extraction using DWT extension. The effectiveness of these techniques is tested on deliberately shifted and rotated palmprints. Further, limited directionality due to DWT is overcome by augmenting with features of contourlet transform. Contourlet transform can extract curve singularities effectively with multidirectional decomposition capability; wavelets are good in extracting point singularities. The different views of contourlet transform and DWT on palmprints motivate us to extract contourlet and wavelet features, and examine them for their individual and combined verification performances. The combined mode is found to perform well over their individual performances. The average EER (0.41%), obtained on PolyU-Online-Palmprint- Database-II (PolyU), is better than the existing wavelets/transform-based palmprint recognition approaches and comparable to the other state of the art palmprint recognition approaches. The computational burden on feature extraction and matching is substantially low thereby making the approach suitable for resource constrained environments. Copyright 2010 John Wiley & Sons, Ltd. KEYWORDS feature extraction; fusion; wavelets; contourlets; palmprint; biometrics * Correspondence S. M. Prasad, Department of CSE, National Institute of Technology Calicut, Kerala , India prasad sm@rediff.com 1. INTRODUCTION The security is emerging as a main concern of today s world. Among the various security issues, personal security is becoming prominent in the applications such as banking, visa issue, access control, and mobile computing devices. Biometric authentication, as compared to the conventional methods, is gaining prominence due to its inherent presentabality and increasing reliability. Biometrics is the measurement of physiological traits such as palmprints, finger prints and iris, and/or behavioral traits such as gait and keystroke of an individual person for personal recognition [1]. Researchers are working on most of these traits and their combinations to enhance the performance: reliability, accuracy, and speed. Palmprint has unique and stable patterns consisting of thick principal lines, moderate sized wrinkles, thin ridges, and minutiae. Moreover, discriminative principal lines and wrinkles can be extracted even from the low resolution images. In brief, palmprint has favorable set of biometric characteristics: user acceptance, distinctiveness, universality, easy to capture, and inexpensiveness [2,3]. The existing feature extraction techniques for palmprint recognition are broadly classified based on lines, appearance, texture, and other features [4,5]. Accuracy and speed are two critical factors in biometric recognition applications. These factors are traded off in most of the palmprint recognition methods. The main objective of this paper is to enhance the accuracy using inherently fast wavelet-based feature extraction. DWT has excellent space-frequency localization and MRA capability apart from the availability of excellent theoretical frame work and fast implementing algorithms [6,7]. Its usefulness in texture classification is well known. Multidirectional and multisized principal lines and wrinkles are prominent features in the low resolution palmprint images. Intuitively, DWT can be a good choice to extract such Copyright 2010 John Wiley & Sons, Ltd. 577

2 Palmprint authentication using fusion of wavelet and contourlet features S. M. Prasad, V. K. Govindan and P. S. Sathidevi patterns. Many researchers demonstrated the usefulness of DWT in palmprint recognition; most of them reported moderate accuracies [8--11]. The major drawbacks of most of the wavelet-based palmprint feature extraction methods are that they are not invariant to translational and rotational changes of the palmprint images. This is due to the inherent shift and rotation variant properties of DWT and palmprint alignment errors. Also, the limited directional decomposition of DWT may not be effective in extracting the multidirectional contour/curve like line structure in palmprints [12]. Contourlet transform is an extension to 2D DWT; recently developed by Do and Vetterli [12]. Multidirectionality, multiresolution, and anisotropy are the important properties of contourlet transform. Contourlets can effectively extract curves/contours. Contourlets see smoothness along the curves/contours; wavelets see smoothness along straight horizontal, vertical, and diagonal (HVD) lines which are seldom present in palmprints [12]. Moreover, contourlet transform is less computationally complex due to the elongated bases with flexible aspect ratio and inherent nonseperability. Despite the usage of effective alignment method to extract the palmprint regions of interest (ROIs), small Rotations and translations, particularly in the intraclass palmprints, cannot be ruled out. This leads to significant reduction in recognition accuracy. This necessitates a robust approach invariant to translations and rotations. The proposed work is an attempt in this direction. We identify two types of translational problems, that cause variations in subband energy distribution, in the DWT-based palmprint recognition systems. Firstly, subsampling causes coefficient perturbations locally even for small variations in ROI [6,13]. Secondly, shift in the corresponding coefficients (singularities) in spatial domain due to positional shift in the line structure (due to alignment errors) in the palmprint ROI [6]. The former problem can be addressed by using computationally efficient shift invariant over complete discrete wavelet transform (OCDWT) [14]. The latter problem is addressed by the proposed nonuniform partition of the detailed subbands, based on the distribution of principal lines, in the spatial domain. HVD detailed subbands emphasize HVD edges/lines, respectively [7]. Integration of HVD subbands would collectively represent edge patterns of palmprint line structure thus preserving global energy. Slightly rotated intraclass palms are likely to have same global energy. Based on this heuristic, rotation problem is addressed by the proposed integration of the intrascale detailed subbands. Further, minimization of the loss of information, in the subsampling process of DWT stage of OCDWT, is proposed to preserve the discriminating ability of the wavelet features. This is termed as modified OCDWT (MOCDWT). Principal lines are thick, smooth, and curved with a few branches. Wrinkles are smooth, short, and medium sized [2]. Many wrinkles intersect principal lines. Wavelets are effective in extracting the point singularities (large change in the adjacent pixel intensities), and piecewise straight edges, especially, when they are exactly horizontal or vertical or diagonal. DWT is used to extract these features in the form of energies. Contourlets can extract curve/contour singularities (smooth curves) with multidirectional and multiscale decomposition of ROI. The different views of wavelets and contourlets on palmprints motivate us to investigate their feature extraction and matching capability under combined mode. The present work proposes an enhanced technique for palmprint authentication incorporating: (i) minimization of information loss due to subsampling in DWT stage of OCDWT, (ii) translation (TI) and rotation invariance (RI), and (iii) fusion of wavelet and contourlet features. The experimental results on PolyU database demonstrate the effectiveness of the proposed techniques in performance enhancement. Also, the experimental results on deliberately translated and rotated ROIs demonstrate the effectiveness of the proposed invariant techniques in minimizing the errors. The remainder of the paper is organized as follows. A brief review of the related work on palmprint recognition approaches is given in Section 2. Section 3 provides an overview of the proposed system. OCDWT and contourlet transform are briefly introduced in Sections 4 and 5, respectively. Feature extraction using OCDWT and contourlet transform, and the process of fusion are described in Section 6. Experiments, results, related discussion, and comparative performance characteristics are presented in Section 7. Finally, Section 8 concludes the work. 2. RELATED WORK Palmprint feature extraction methods may be broadly classified based on lines [15--17], appearance [18--21] and texture [8--11,22--26] feature extraction, and others [5,27--30]. Prominent works in line based methods employ exclusive principal lines and wrinkles extraction using directional masks and edge detectors [15,16], and modified Radon transform [17]. Line feature extraction and matching is a difficult task and often inaccurate [21]. Appearance based or subspace methods include usage of principal component analysis (PCA), independent component analysis (ICA), linear discriminant analysis (LDA), and combination of their variants. These techniques are mostly based on projection of the high dimensional palmprint data onto the low dimensional subspace; resulting subspace coefficients are treated as features. Some researchers embed wavelets [11,20], discrete cosine transform (DCT) [19], and locality preservation [21] into the appearance-based methods in their works. They are all mostly dimensionality reduction techniques. Gabor filters and 2D Gabor phase coding were used to extract texture information for matching in [29]. Hennings et al. [28] used advanced correlation filter-based palm specific classifiers. Kong et al. [27] employed elliptical Gabor filters in different orientations to extract Fusion code. Kumar and Zhang [5] extracted texture, line, and PCA features from the same ROI; fused based on different fusion strategies. Jia et al. [30] employed modified finite 578 Security Comm. Networks 2011; 4: John Wiley & Sons, Ltd.

3 S. M. Prasad, V. K. Govindan and P. S. Sathidevi Palmprint authentication using fusion of wavelet and contourlet features Radon transform for robust line orientation code extraction; computationally complex pixel to area comparison for matching. Accuracy and speed are traded off in most of these methods. The detailed analysis of these methods is beyond the purview of this paper. This paper emphasizes wavelet-based feature extraction. Texture feature extraction methods generally transform the palmprints onto the frequency domain; features are then extracted and represented, generally, using statistical measures. Such methods, also known as transform-based methods, are computationally simple and suitable for real time applications. The proposed method falls under this category. Li et al. [23] applied discrete Fourier transform (DFT) to extract energy features in frequency domain to classify the palmprints. Kumar and Zhang [24] used DCT to extract the texture features in the form of localized DCT coefficients. DCT and DFT-based methods reported poor accuracies due to lack of space-frequency localization and MRA capabilities. Zhang and Zhang [9] used shift invariant Overcomplete wavelet expansion (OWE) to extract the principal line features in the form of wavelet coefficients. They also used wavelet coefficient context modeling to extract the line features; defined a set of wavelet-based statistical signatures for feature representation. Their work considers only global features, mostly ignoring wrinkles, and hence reported low accuracy even on a small sized database. Wu et al. [10] employed DWT and partitioned the subbands to extract wavelet energy features. In Reference [10], the detailed subbands are partitioned into non-overlapping equal sized square blocks which can reduce intraclass errors. However, due to oversize of the block, interclass errors are likely to increase. This is due to the possible overlapping of energies corresponding to the lines of interclass palms on the same block region. Undersize of the block, on the other hand, increases the intraclass errors. Chen and Xie [25] applied approximately shift invariant dual tree complex wavelet transform (DTCWT) and 2D DFT on decomposed subbands for feature extraction; SVM for classification. Chen and Kegl [26] attempted to use contourlet transform and Fourier transform to classify the palmprints. Dong et al. [31] used digital curvelet transform, which is combination of wavelet transform and ridgelet transform, for feature extraction. Butt et al. [32] applied contourlet transform for feature extraction and reported low EER using very large training set, different preprocessing, and unusual experimenting procedure. The prominent DWT-based works ignored translation and rotational problems in their works. This prompted us to propose the translation and RI techniques for palmprint recognition system to enhance the robustness. EER is reduced by minimizing the loss of information due to subsampling, which was hitherto ignored in the previous works. The EER is further reduced by fusing the wavelet and contourlet features at different levels. 3. OVERVIEW OF THE PROPOSED SYSTEM The schematic diagram of the proposed approach is shown in Figure 1. It includes preprocessing, feature extraction, matching, and classification stages. Preprocessing includes segmentation, location of invariant points, alignment, ROI extraction, lowpass filtering, and equalization. Even the good ROI extraction algorithm [29] employed may not prevent translations and rotations in ROIs. Feature extraction stage uses the proposed method to create feature templates of palmprints. That is, the palmprint template is created by combining the normalized palmprint features extracted using MOCDWT with the proposed translation and rotation invariant techniques (MOCDWT(TI + RI)) and contourlet Figure 1. Block diagram of the proposed approach. Security Comm. Networks 2011; 4: John Wiley & Sons, Ltd. 579

4 Palmprint authentication using fusion of wavelet and contourlet features S. M. Prasad, V. K. Govindan and P. S. Sathidevi transform. In the enrolment stage, registered database containing palmprint templates is created and threshold for classification is determined using the training templates. In the verification stage, the test template is compared (matching) with the registered template of claimed identity to decide on the claimed identity (classification). The match scores are generated for MOCDWT(TI + RI) and contourlet (CNT mode) features individually and in combined mode. Feature level and score level fusion schemes are employed to classify the image as genuine or impostor. M 0. { ( ) Aj h x 2 hy 2 if j<m A j+1 = ( A j h j M x ) h j M y otherwise { ( ) W H j+1 = Aj h x 2 gy 2 if j<m ( A j h j M x ) g j M y otherwise { ( ) W V j+1 = Aj g x 2 hy 2 if j<m ( A j g j M x ) h j M y otherwise (1) (2) (3) 4. OVER COMPLETE DISCRETE WAVELET TRANSFORM (OCDWT) DWT is inherently translation variant mainly due to subsampling at every scale [13]. DWT is also not rotation invariant. This leads to perturbations in detail coefficients locally even for a small shift in the input signal/image [6,13]. There are various techniques to minimize the shift variance of DWT. Among them, stationary wavelet transform (a trous algorithm) is efficient but computationally complex. Other shift invariant techniques include DTCWT [13] and OCDWT [14]. We choose OCDWT that has the computational efficiency and sparse representation inherent in the critically sampled Mallat algorithm and the shift invariance inherent in the fully sampled a trous algorithm [14]. The approach used in the proposed work is to apply the Mallat algorithm to the first M levels of an L level decomposition and then apply the a trous algorithm to the remaining (L-- M) levels. OCDWT can be seen as a generalization of the DWT, that produces the conventional DWT when M = L and produces the fully sampled a trous algorithm when M = 0. The following convolution formulae (1--4) are iterated to compute the dyadic OCDWT for 2D signals with j 0, { ( ) W D j+1 = Aj g x 2 gy 2 ( A j g j M x ) g j M y if j<m otherwise where 2 and * denote subsampling by 2 and convolution operation, respectively. j indicates decomposition scale. h x, h y, and g y, g x are lowpass and high pass analysis filters, respectively (x-row, y-column). The subbands labeled A, W, H, V, and D represent approximate, detail, HVD subbands, respectively. Note that for j M, 2 j- M --1 zeros must be padded between the jth scale filter coefficients in order to dilate the mother wavelet to restore the filter bandwidth. This is denoted as superscript (j--m) in the filter transfer functions. The functional diagram of the OCDWT is shown in Figure CONTOURLET TRANSFORM Do and Vetterli [12] recently developed the contourlet transform to overcome some of the limitations of wavelets: anisotropy, directionality, and capturing of smooth curves. Contourlet transform is implemented using double filter bank structure for obtaining sparse expansions for typical images having smooth curves/contours. In this double filter (4) Figure 2. Functional block diagram of OCDWT. 580 Security Comm. Networks 2011; 4: John Wiley & Sons, Ltd.

5 S. M. Prasad, V. K. Govindan and P. S. Sathidevi Palmprint authentication using fusion of wavelet and contourlet features Figure 3. Functional block diagram of contourlet decomposition. bank, the Laplacian pyramid (LP) is first used to capture the point discontinuities, and then followed by a directional filter bank (DFB) to link point discontinuities into linear structures. The over all result is an image expansion using basic elements like contour segments, and, thus named as contourlets. Also, contourlets have elongated supports at various scales, directions, and aspect ratios. This allows contourlets to efficiently approximate a smooth contour at multiple resolutions. Figure 3 shows a multiscale and directional decomposition using a combination of an LP and DFB. Bandpass images from the LP are fed into DFB so that directional information can be captured and this scheme can be iterated on lowpass coarse image resulting directional subbands at multiple scales. Specifically, let a 0 [n] bethe input image. The output after the LP stage is J bandpass images, b j [n], j = 1, 2...J (fine to coarse order) and a lowpass image. That means, the jth level of the LP decomposes the image a j 1 [n] into a coarser image a j [n] and a detail image b j [n]. Each bandpass image b j [n] is further decomposed by an l j level DFB into 2 lj bandpass directional images. More on this is available in [12]. Figure 4 shows the decomposition of ROI using DWT and contourlet transform. 6. FEATURE EXTRACTION AND FUSION The feature extraction and fusion process involves the following stages (1) Feature extraction: using OCDWT, (2) Feature extraction: using contourlet transform, and (3) Fusion: feature level and match score level fusion of these features.these are explained in Sections Figure 4. (a) Palmprint ROI. (b) 3 level DWT decomposition of (a). (c) 3 level (8, 4, and 2 direction (fine to coarse levels, respectively)) contourlet decomposition of (a). Security Comm. Networks 2011; 4: John Wiley & Sons, Ltd. 581

6 Palmprint authentication using fusion of wavelet and contourlet features S. M. Prasad, V. K. Govindan and P. S. Sathidevi 6.1. Feature extraction: Using OCDWT The feature extraction using OCDWT involves the following steps (1) Modification of detailed subband coefficients (MOCDWT). (2) Non-uniform partition of detailed subbands: Translation invariant feature vector formation (F TI ). (3) Integrated subband (ISB) formation: Rotation invariant feature vector formation (F RI ).These are described briefly in the following Sections Modification of detailed subband coefficients (MOCDWT). Palmprint ROI is decomposed using OCDWT with M = 2; subsampling exists for j < M. Subsampling is a process of discarding the alternative samples of analysis filters output in DWT stage. Sampling theorem allows the reconstruction of the original signal using inverse transformation. In the proposed feature extraction application, where the question of inverse transformation does not arise, subsampling can be treated as loss of information in the spatial domain. We propose to minimize this loss by averaging the two consecutive samples instead of discarding one of them outrightly. This is depicted in Figure 5 and expressed using (5--7). The averaging is done along columns, and not along rows, to avoid changes in the coefficient values for column level convolution. ( y h j+1 [n, k] = gj [2n, k] + ygj h [2n + 1,k]) 2 W Hm (5) ( g y W Vm j+1 [n, k] = hj [2n, k] + yg hj [2n + 1,k]) 2 ( g y W Dm j+1 [n, k] = gj [2n, k] + y g gj [2n + 1,k] ) 2 where Wj+1 Hm Dm [n, k],wvm j+1 [n, k], and Wj+1 [n, k] represent modified HVD subband coefficients, respectively. yh h [2n, k] and yh h [2n + 1,k] are two consecutive coefficients. h and g represent lowpass and high pass analysis filters, respectively, and j denotes the decomposition scale. Superscript indicates filter type of prior (row) decomposition; subscript indicates column decomposition filter. The detailed subbands thus obtained are termed as modified detailed subbands (MDSB) and the corresponding transform is referred as MOCDWT Non-uniform partition of HVD subbands: translation invariant feature vector formation (F TI ). Spatial partition of subbands considers the spatialfrequency distribution of energies. In Reference [10], the subbands are partitioned into uniform sized square blocks. We found that the performance was optimum in [10] for the partition of each subband into 16 equal sized blocks; each of pixels in the first scale and proportionally in the subsequent scales. In Reference [10], we identified that the block size is over estimation as translations in ROIs, generally, do not exceed by 6--7 pixels in ROI. This large sized block minimizes intraclass errors. However, it increases the interclass errors due to the possible overlapping of energies of lines of different palms in the same block region. The palmprint line structure is non-uniformly distributed; horizontal head and heart lines are likely to lie in (6) (7) Figure 5. MOCDWT: modification of detailed subband coefficients in DWT stage of OCDWT. 582 Security Comm. Networks 2011; 4: John Wiley & Sons, Ltd.

7 S. M. Prasad, V. K. Govindan and P. S. Sathidevi Palmprint authentication using fusion of wavelet and contourlet features subbands at different scales. Integration of HVD subbands, using (11), would collectively represent edge patterns of palmprint line structure thus preserving the global energy. The subband thus formed is termed as ISB. W ISBm j [m, n] = ( W Hm j [m, n] +Wj Vm [m, n] +Wj Dm [m, n] ) 3 (11) Figure 6. Non-uniform partition. (a) ROI. (b) Intensity enhanced first level horizontal subband of (a). upper half of ROI in most of the palmprints; diagonal or vertical life line is likely to lie in the middle of the lower half of ROI. Based on this observation, we propose to have the block size to be non-uniform in its size and orientation: small-horizontal blocks in the upper half of ROI; smallvertical blocks in the middle of the lower half of ROI; and large-square blocks in the rest of the ROI. This is termed as non-uniform partition and is shown in Figure 6. Normalized energies of all the blocks of all MDSBs at different scales are computed using (8--10) and represented as F TI : translation invariant feature vector. Further, the normalized energies of all the blocks of over complete subbands are also appended to F TI. In the following equations, m and n are dimensions of the partitioned block that vary based on the orientation and size of the block and decomposition scale. E Hm ij = E Vm ij = E Dm ij = m,n m,n m,n ( W Hm ij [m, n] ) 2 ( W Vm ij [m, n] ) 2 ( W Dm ij [m, n] ) 2 (8) (9) (10) where E ij and W ij are ith block jth scale energy and subband coefficients, respectively Integrated subband (ISB) formation: rotation invariant feature vector formation (F RI ). The HVD subbands emphasize HVD line energies, respectively [7]. Perfect HVD line structure will be captured in the respective subbands. Capturing of other lines will be shared among all the subbands depending on the extent of changes/singularities in different directions. The line structure in palmprints is multidirectional and multisized; unlikely to have straight lines. The energies corresponding to these lines will be shared among different intrascale where Wj ISBm [m, n] is ISB. Rotation in the image leads to change in the distribution of coefficient energies corresponding to line structure in different subbands at the same scale. Slightly rotated intraclass palms are likely to have same global energy and hence the energy in the corresponding ISB remains almost invariant (see Figure 7). The ISBs of different scales are further non-uniformly partitioned into blocks to compute the rotation invariant normalized energy features using (12) and are represented by feature vector F RI. F TI and F RI are combined to form the wavelet based feature vector F w. E ISBm ij = To summarize: m,n ( W ISBm ij [m, n] ) 2 (12) (1) Decompose the ROI using OCDWT, with M = 2 and L = 5, and modify the detailed subband coefficients (along columns) at each scale in DWT stage to obtain MDSBs. (2) Form the ISB by averaging the intrascale HVD subbands in DWT stages. (3) Non-uniformly partition each subband (including ISB, overcomplete stage HVD subbands and second scale approximate subband) into blocks. (4) Form the feature vector containing the normalized energies of all the blocks of all MDSBs, subbands of over complete decomposition and ISBs: F W = [F TI, F RI ]. (5) This feature vector is used for training and testing (MOCDWT (TI + RI) mode) Feature extraction: using contourlet transform The ROI image is decomposed using the suitable Laplacian and directional filters into multiple levels. At each level the subbands are partitioned into blocks with the similar procedure used in partitioning subbands of OCDWT (Section 6.1.2). The normalized energy of each block is considered as a feature. A feature vector is constructed comprising of the energies of all the blocks of all levels and directions. To summarize: (6) Decompose the ROI using contourlet transform with suitable scale and directions. Security Comm. Networks 2011; 4: John Wiley & Sons, Ltd. 583

8 Palmprint authentication using fusion of wavelet and contourlet features S. M. Prasad, V. K. Govindan and P. S. Sathidevi Figure 7. ROI rotated by (a) 0, (b) 3, (c) 6, and (d) 9, and the corresponding first level ISBs (lower row). (7) Partition the each directional subband at every level into blocks. (8) Form the feature vector containing the normalized energies of all the blocks of all subbands of all levels and directions: F CT. (9) This feature vector is used for training and testing (CNT mode) Fusion DWT and contourlet transform view palmprints differently; wavelets extract point singularities while contourlets extract curve singularities effectively. Moreover, at every scale, contourlet transform provides image details in multiple directions while wavelets provide only in three directions. We employ feature level and score level fusion techniques. Under score level fusion mode, we employ both sum rule; suitable for fusion of correlated features, and product rule; suitable for complementary/weakly correlated features [5]. To summarize: (10) Feature level fusion: normalize and concatenate F W and F CT ; F COM = [F NW, F NCT ], where F COM, F NW, and F NCT are fused, normalized wavelet and contourlet feature vectors, respectively. (11) Score level fusion: the match scores obtained in the step 5 and 9 are normalized and fused using product and weighted sum rule for classification. 7. EXPERIMENTS, RESULTS, DISCUSSION, AND COMPARISON 7.1. Experiments: database and performance metrics Experiments were performed on well-known PolyU-onlinepalmprint-II database [34]. This database contains low resolution (75 dpi) palmprints from 386 classes captured using CCD camera. Every class has approximately 10 images captured from each of two sessions. False rejection ratio (FRR), false acceptance ratio (FAR), equal error rate (EER), and receiver operating characteristics (ROC) are the four important performance metrics in recognition systems. FRR is the frequency with which genuine person is rejected. FAR is the frequency with which the impostor is accepted. ERR is the error rate at which FAR and FRR become equal. ROC is the plot of FRR versus FAR. Recognition rate or accuracy (approximately equal to 1--EER) is the ratio of number of correct matches (comparisons) to total number of matches at EER in the verification systems. We use EER to interpret the performance of the proposed system. The experiments were conducted on Intel P-IV, 1.6 GHz, 512MB RAM, Windows2000, Matlab 2006a platform Verification experiments Verification is comparing a particular palmprint against the claimed identity; also known as one to one comparison. All the images of first session were considered for experimentation. The 10 images per class are further partitioned into four different training and testing datasets (or trials): (a) 3 image/class for training and remaining 7 images/class for testing; (b) 4 image/class for training and remaining 6 images/class for testing; (c) 5 image/class for training and remaining 5 images/class for testing; and (d) 6 image/class for training and remaining 4 images/class for testing. The feature vectors are formed using the proposed feature extraction techniques (OCDWT: five level decomposition with M = 2 using Haar wavelet. Contourlet transform: 9--7 and pkva filters in LP and DFB stages, respectively). Feature vectors of training set are compared with that of test set in every trail. There are comparisons (match scores) out of which 9650 are genuine comparisons and the rest are impostors in trial (c). 584 Security Comm. Networks 2011; 4: John Wiley & Sons, Ltd.

9 S. M. Prasad, V. K. Govindan and P. S. Sathidevi Palmprint authentication using fusion of wavelet and contourlet features Table I. Average EER and feature extraction time of various modes of the proposed approach and DWT [10] approach. DWT MOCDWT MOCDWT MOCDWT Method (Wu et al.[10]) OCDWT MOCDWT (TI) (RI) (TI + RI) CNT Average EER (%) Average feature extraction time (ms) The match score is said to be genuine if the comparison is between test and training feature vectors of the same class, otherwise impostor. Similarity score (1-normalized Euclidean distance) is used as match score. The average EER and average feature extraction time obtained for various OCDWT based and contourlet based feature extraction techniques are given in Table I; corresponding ROC plots are shown in Figure Invariance testing experiments The proposed translation and RI techniques are experimented with deliberately translated, rotated, and translatedrotated versions of the palmprints. First three samples (three trials) of each of the 50 palmprint classes are considered for this purpose. During ROI extraction stage, each of the 50 palmprint samples was altered according to the following patterns (for each sample). (a) Translation or shift: no translation, left alone, right alone, up alone, down alone, left-up, right-up, left-down, and right-up by 0, 4, and 8 pixels (all combinations) giving 5 5 = 25 (left-right loop [ 8:4:8] and up-down loop [ 8:4:8]) ROIs per class; totally 1250 translated ROIs. (b) Rotation: By 0, ±1, ±2, ±3... ± 9 giving ( ) = 19 (rotation loop [ 9:1:9]) ROIs per class; totally 950 rotated ROIs. (c) Translation-rotation: {(rotation loop [ 4:4:4]) (translation: left-right loop [ 4:4:4]) (translation: up-down loop [ 4:4:4])} giving 27 ROIs per class; totally 1350 translated-rotated ROIs. Verification experiments are conducted on these altered datasets with and without the proposed translation and RI techniques. The average EERs obtained over three trails are given in Table II; corresponding ROC plots are shown in Figure 9. The average EER of rotated ROIs is more as we considered wide range of rotation angles. The average EER of translated-rotated ROIs obtained is low due to smaller extent of translations and rotations. proposed invariant techniques show consistent reduction in the EER irrespective of the extent of rotations and translations Fusion The CNT and MOCDWT (TI + RI) features were examined for their individual and combined performance. Under combined performance test, feature level fusion and score level fusion strategies were used to classify the palmprint as genuine or impostor. In feature level fusion, the CNT and MOCDWT (TI + RI) features were normalized in order to maintain the common range. The min--max normalization rule, considered as simple and efficient normalization rule [33], was employed in our approach and it is given in (13). f = f min(f) max(f) min(f) (13) Figure 8. ROC plots for various modes. (a) DWT, OCDWT, MOCDWT, MOCDWT (TI + RI), and CNT. (b) MOCDWT with the proposed invariant techniques. Security Comm. Networks 2011; 4: John Wiley & Sons, Ltd. 585

10 Palmprint authentication using fusion of wavelet and contourlet features S. M. Prasad, V. K. Govindan and P. S. Sathidevi Table II. Average EER: translated, rotated, and translated-rotated ROIs. ROIs Translated ROIs Rotated ROIs Translated -rotated ROIs Feature extraction technique used MOCDWT(TI) MOCDWT(RI) MOCDWT (TI + RI) Average EER (%) Without the proposed invariance technique With the proposed invariance technique Table III. Average EER: MOCDWT, CNT, and combined modes. Method MOCDWT (average EER (%)) CNT (average EER (%)) Combined mode (average EER (%)) Score level fusion Feature level fusion Sum rule Product rule Without TI and RI With the proposed TI and RI techniques where, f, f, and F are feature, normalized feature and feature vector, respectively. We also investigated the combined mode performance using score level fusion as it is considered as effective for correlated features [5]. Both weighted sum and product rules were employed. Product rule performed better with the lowest average EER for eight orientations/directions in each contourlet decomposition level. With fewer (less than eight) contourlet orientations, sum rule had shown slightly better performance. The EERs of combined mode are given in Table III and the corresponding ROC plots are shown in Figures 10 and 11. Figure 9. ROC plots of deliberately altered ROIs with and without the proposed invariant techniques: (a) translated ROIs, (b) rotated ROIs, and (c) translated -rotated ROIs. 586 Security Comm. Networks 2011; 4: John Wiley & Sons, Ltd.

11 S. M. Prasad, V. K. Govindan and P. S. Sathidevi Palmprint authentication using fusion of wavelet and contourlet features Figure 10. ROC plots of combined (fusion) mode. (a) Fusion of CNT and MODCWT (without TI and RI) features. (b) Fusion of CNT and MOCDWT (RI + TI) features Results An attempt is made to overcome the limitations of the DWTbased palmprint verification approaches in three stages. Firstly, translation and rotation invariant feature extraction techniques are proposed to enhance the robustness and hence the accuracy. Translation invariance is achieved using translation invariant OCDWT and non-uniform partition of ROIs. Approximate RI is achieved through the proposed integration of the intrascale HVD subbands at all scales of DWT. The individual and combined effect of these techniques is tested on deliberately translated and rotated ROIs. The translation and RI techniques could reduce the average EER by and 9.84%, respectively. Incorporating both the techniques could reduce the average EER by 9.02% with negligible computational burden (Table II). The important observation is that the reduction is consistent (Figure 9). Employing the invariant techniques on the actual datasets could reduce the average EERs: 10.97% by translation invariant technique; 3.48% by rotation invariant technique; and 16.30% by both (Table I and Figure 8). The relative improvement in the overall performance on actual datasets could be due to non-uniform and lower degree of translations and rotations caused by the good alignment technique. From the ROC plots (Figures 8 and 9(a) and (b)), it is evident that the translation invariance technique is more effective than RI technique. Nevertheless, the combination of these techniques is effective in reducing the average EER (Figures 8 and 9(c)) irrespective of the datasets. Secondly, presence of DWT stages in OCDWT allows us to average the consecutive samples, instead of subsampling, to minimize the loss of information. This is effective and could reduce the average EER: 6.83% without translation and RI techniques; and 22.02% with translation and RI techniques (Table I and Figure 8). Thirdly, by fusing the wavelet and contourlet features. It is observed that match score fusion under sum rule performed slightly better for fewer contourlet directional decompositions (results are not reported). This can be attributed to the correlation nature of the wavelet and contourlet features when the number of directional decompositions is approximately same, and suitability of sum rule for correlated features [5]. However, performance of the product rule is superior for more number of contourlet directional decompositions. This can be attributed to the weak correlation/complementariness of contourlet features with Figure 11. (a) Impostor and genuine score distribution plot of score fusion: Product rule (MOCDWT (TI + RI)CNT)) (lowest EER). (b) ROC plot of MOCDWT (TI + RI), CNT, PROPOSED (score fusion: product rule of (MOCDWT (TI + RI)CNT) (lowest EER)), and Wu et al.[10] approach. Security Comm. Networks 2011; 4: John Wiley & Sons, Ltd. 587

12 Palmprint authentication using fusion of wavelet and contourlet features S. M. Prasad, V. K. Govindan and P. S. Sathidevi Table IV. Comparison of the proposed method with various palmprint recognition approaches. Sl. no Category Researchers Method EER (%) Accuracy (%) Number of classes (database) and palmprint image resolution 1 Wavelet Based Zhang and Zhang [9] OWE FRR = 0 FIR = (65 dpi) Methods (2004) 2 Wu et al. [10] (2005) DWT (75 dpi) 3 Nanni and Lumini [11] DWT, PCA, ICA, 2.68 NR 72 (2008) LEM 4 Other Methods Pan and Ruan [21] ocality NR (72 dpi) (2008) preservation, PCA 5 Pan and Ruan [22] Gabor-based local NR (72 dpi) (2009) invariant features 6 Proposed OCDWT and Contourlet Transform (75 dpi) Note: NR, not reported; FIR, false identification rate; LEM, Laplacian eigen maps. wavelet features and suitability of product rule [5]. Feature fusion is always inferior to score fusion: product rule. Nevertheless, consistent reduction in the average EER is observed due to fusion in all the cases (including invariant cases): 32.00% in feature fusion; 30.63% in sum rule and 37.09% in product rule. The verification time in feature fusion, product rule and sum rule were found to be approximately 40, 60, and 88 µs, respectively, thus making the system very much suitable for fast identification applications also Discussion and comparison Table IV provides the summary of the comparison of the proposed approach with the prominent wavelet based and other state of the art approaches. For fair comparison, the approaches experimented using the databases containing the palmprint images captured in a single session and comparable resolution are considered. Further, the reported results from the competent approaches are chosen so as to avoid the possible unfairness in the comparison due to implementation uncertainties. Our approach is better among the wavelet based approaches with respect to EER. Wu et al. [10] reported 0.76% verification EER on 320 palmprint classes of the same session. Our implementation of Wu s approach, on the datasets we used, yielded an average EER of 1.99%. The higher EER of Wu s approach, in our implementation, could be due to more number of classes and different ROI extraction algorithm. In Zhang and Zhang [9], the reported classification accuracy (98%), even on a small sized dataset (50 classes), is lower than that of our approach. Nanni and Lumini [11] reported higher EER (2.68%) on 100 classes using dimensionality reduction feature extraction and feature selection algorithm. We choose prominent other state of the art approaches [21,22], and reported results thereon, to compare the performance of our approach. The researchers in [21,22] used the images captured in one session to experimentally validate their methods. Pan and Ruan [21] reported lower accuracies despite using the computationally complex dimensionality reduction technique. In Reference [22], the ROIs were filtered using Gabor filters before extracting the local invariant features and reported lower accuracy (Table IV). A few state of the art approaches reported better EER on polyu palmprint database using images of both the sessions. Such methods are likely to perform better than our approach [17,30]. However, the feature extraction and matching time of such methods [17,30] are expensive than that of our approach. Feature extraction time is more in [17,30] due to the application of Radon transform, and matching time is more in [30] due to pixel to area matching. In Reference [17], the reported feature extraction time is 430 ms, and in [30], the reported matching time is 3.9 ms (on a relatively high speed machine). The performance of our approach is likely to degrade when images of two different sessions are used due to non-uniform illumination between images of the first session and second session. This could be due to energies of false singularity points that separate normal regions and abnormally illuminated (bright/shadow) regions on ROIs. 8. CONCLUSIONS In this paper, approximately translation and rotation invariant palmprint feature extraction approach, based on DWT extension, is proposed. It is experimentally shown that non-uniform partition of HVD subbands in spatial domain provides translation invariance; integration of 588 Security Comm. Networks 2011; 4: John Wiley & Sons, Ltd.

13 S. M. Prasad, V. K. Govindan and P. S. Sathidevi Palmprint authentication using fusion of wavelet and contourlet features intrascale HVD subbands provides rotational invariance. The effectiveness of these invariant techniques is tested on deliberately translated-rotated ROIs. Minimization of information loss due to the subsampling in DWT stage is effective in reducing EER even when translation and rotation invariant techniques are employed. Further, fusion of wavelet features and contourlet features, under feature level and score level, improves EER and hence the accuracy of the system. The improvement in EER is relatively better in score fusion: product rule for more contourlet directional decompositions. The proposed approach is better than the prominent state of the art wavelet-based approaches. Also, in terms of EER, it is better than some of the state of the art computationally complex approaches. When compared to other state of the art palmprint verification approaches that reported comparable EER, our method performs better in terms of feature extraction and matching time. ACKNOWLEDGEMENTS The authors are thankful to Biometrics Research Centre, The Hong Kong Polytechnic University, for providing the PolyU Palmprint Database. REFERENCES 1. Pankanti S, Bolle RM, Jain AK. Biometrics: the future of identification. IEEE Computer 2000; 33(2): Zhang D. Palmprint Authentication. Kluwer Academic Publishers: Dordrecht, the Netherlands, 2004; Jain AK, Ross A, Pankanti S. Biometrics: a tool for information security. IEEE Transactions on Information Forensics and Security 2006; 1(2): Kong A, Zhang D, Kamel M. A survey of palmprint recognition. Pattern Recognition 2009; 42: Kumar A, Zhang D. Personal authentication using multiple palmprint representation. Pattern Recognition 2005; 38: Mallat S. Zero-crossings of a wavelet transform. IEEE Transactions on Information Theory 1991; 37(4): Daubechies I. Ten Lectures on Wavelets. SIAM: PA, 1992; Wu XQ, Wang KQ, Zhang D. Wavelet based palmprint recognition. Proceedings of the First International Conference on Machine Learning and Cybernetics, 2002; Zhang L, Zhang D. Characterization of palmprints by wavelet signatures via directional context modeling. IEEE Transactions on Systems, Man, and Cybernetics- Part B: Cybernetics 2004; 34(3): Wu XQ, Wang KQ, Zhang D. Wavelet energy feature extraction and matching for palmprint recognition. Journal of Computer Science and Technology 2005; 20(3): Nanni L, Lumini A. Wavelet decomposition tree selection for palm and face authentication. Pattern Recognition Letters 2008; 29: Do MN, Vetterli M. The contourlet transform: an efficient directional multiresolution image representation. IEEE Transactions Image on Processing 2005; 14(12): Selesnick IW, Baraniuk RG, Kingsbury NC. The dualtree complex wavelet transform. IEEE Signal Processing Magazine 2005; 22(6): Bradley AP. Shift-invariance in the discrete wavelet transform. Proceedings of VIIth Digital Image Computing: Techniques and Applications, 2003; Wu X, Wang K, Zhang D. Line feature extraction and matching in palmprint. Proceeding of the Second International Conference on Image and Graphics, 2002; Wu X, Wang K, Zhang D. A novel approach of palm-line extraction. Proceeding of the Third International Conference on Image and Graphics, 2004; Huang D, Jia W, Zhang D. Palmprint verification based on principal lines. Pattern Recognition 2008; 41: Lu G, Zhang D, Wang K. Palmprint recognition using eigenpalms features. Pattern Recognition letters 2003; 24: Jing XY, Zhang D. A face and palmprint recognition approach based on discriminant DCT feature extraction. IEEE Transactions on Systems, Man, and Cybernetics- Part B: Cybernetics 2004; 34(6): Lu GM, Wang KQ, Zhang D. Wavelet based independent component analysis for palmprint identification. IEEE Proceedings of International Conference on Machine Learning and Cybernetics 2004; 6: Pan X, Ruan Q. Palmprint recognition with improved two-dimensional locality preserving projections. Image and Vision Computing 2008; 26: Pan X, Ruan Q. Palmprint recognition using Gaborbased local invariant features. Neurocomputing 2009; 72: Li W, Zhang D, Xu Z. Palmprint identification by Fourier transform. International Journal of Pattern Recognition and Artificial Intelligence 2002; 16(4): Kumar A, Zhang D. Personal recognition using hand shape and texture. IEEE Transactions on Image Processing 2006; 15(8): Chen GY, Xie WF. Pattern recognition with SVM and dual-tree complex wavelets. Image and Vision Computing 2007; 25: Security Comm. Networks 2011; 4: John Wiley & Sons, Ltd. 589

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