Palmprint Recognition Based On Bit-Plane Extraction

Transcription

1 Palmprint Recognition Based On Bit-Plane Extraction T.Z. Lee and D.B.L. Bong Faculty of Engineering Universiti Malaysia Sarawak Kota Samarahan MALAYSIA Abstract: - In this paper, a palmprint recognition system using bit-plane extraction with MLP neural network is presented. It is an approach where the palmprint feature is extracted by slicing the image into 8 bit-planes. The extracted bit-planes are then serves as input data into the neural network. MLP neural network is applied to train and test images for recognition. Networks are simulated for a few configurations and the results are compared. Performance is evaluated by comparing recognition rates between single bit-plane and benchmarked gray-level on dataset of 100 samples. FAR, FRR and HTER are also calculated to evaluate the recognition rate. Key-Words: - Palmprint recognition, bit-plane feature extraction, image processing, multilayer perceptron (MLP), pattern recognition. 1 Introduction Palmprint biometric technology is one of the most reliable biometric technologies that can provide higher verification performance due to the uniqueness of palmprint possess by each individual. Compared to other biometric technologies such as face and fingerprint recognition, palmprint biometric provides several benefits. Firstly, palmprint authentication can be deployed by using low resolution imaging. Hence, the palmprint acquisition device might cost lesser than an iris-scanning device. Basically, both high resolution and low resolution images are employed in palmprint. They only differ in the applications to be applied on. High resolution images are usually suitable for forensic applications, for instance, criminal detection [1] whereas low resolution images mostly applied for access control in civil and commercial applications. Generally, 400 dpi or more is referred as high resolution while 150 dpi or less is the low resolution image [2]. Also, palmprint is harder to imitate than fingerprint. Palmprint biometric system can provide lower intrusiveness since it can achieve higher accuracy. Besides, it is also more acceptable than face recognition system that may cause privacy issues. Thus, this makes palmprint biometric a higher user acceptance technology [3][4]. There are various types of features in palmprint image. Feature extraction then depends on the targeted feature. Some suggested in [5] to combine two features, for instance, line and geometry features to create a new type of feature which is the line geometry feature. The extracted feature is then transformed into a reduced representation set of features or namely feature vector so that comparison can be done easily in later stage [6]. For feature extraction, several researches had been conducted and they came out with various methods pertaining to this topic. Generally, feature extraction is based on several methods which include texture, line, subspace, local statistical, global statistical and coding based. In line based extraction, mostly it applies edge detector and some existing methods, for instance, fuzzy directional element energy feature (FDEEF) proposed by Xiangquan Wu et al. [7]. It is modified from one of the Chinese character recognition method that is the Directional Elements Feature (DEF) to extract the line structural of the palm. In 2011, G.Rigatos and Q.Zhang [8] came out with a method based on local statistical approach which is by using a fuzzy model validation. There are two stages in the statistical local approach: i) the global x2 test that detects changes in parameters of fuzzy model and ii) diagnostics test which separate affected parameters due to the change in the fuzzy model. In addition, Wang et al. applied boosted statistical local feature Local Binary Pattern (LBP) histogram based classifiers as a novel approach for palmprint identification [9]. Local Binary Pattern ISBN:

2 (LBP) is a powerful texture descriptor that is grayscale and rotation invariant [10]. They proposed to use local binary pattern based features as they believe that this feature is very discriminative especially for palmprint recognition. In their approach, they modified and improved the method proposed by Timo et al [11] which is also applying the local binary pattern histogram for face recognition. Wang et al applied AdaBoost [12] algorithm to select a more discriminative subwindows for classification and Chi square distance for weak classifiers. The proposed method yields results of equal error rate of 2% that is comparable to that of the PalmCode method [4] through experimenting on UST_HK palmprint database. Furthermore, methods that were proposed for palmprint feature extraction are commonly Sobel and morphological operations [4], derivative of Gaussian techniques [13], Fourier transform [4], Discrete Cosine Transform [14], Wavelet Transform [15] and etc. Bit-plane extraction is therefore a new approach for palmprint feature extraction. Bit-plane extraction method is recently proposed by [16] on face recognition and it yields a high percentage of recognition performance. Thus, in this paper, a new feature extraction approach based on bit-plane information is proposed for palmprint recognition. Bit-plane feature extraction for palmprint is proposed to test out whether this new approach is applicable for palmprint recognition. The main contributions can be summarized as follows: (1) bit-plane feature extraction is proposed where bit information from different bit level in an image is extracted out. As digital image consists of 8 bit, it will yields out 8 single bit-planes. (2) extracted bit information from each plane is then fed into MLP neural network for training and classification. Recognition rates and error rates are obtained here. Conclusion will be made based on the result obtained. (3) Experiments are carried out on dataset of 100 samples from Hong Kong PolyU Palmprint Database [17]. The rest of the paper is organized as follows: Sect.2 describes the overview of bit-plane information. Sect.3 provides the methodology proposed for the palmprint recognition system. In Sect.4, analysis on the experimental results is discussed. Finally, Sect.5 draws the conclusion. 2 Bit-Plane Information Bit-plane information is a digital image which is constructed by multilevel information of bits. The interesting aspects of an image can be highlighted by isolating particular bits of the pixel values. Lower order bits contain subtle details while higher order bits usually consists of most of the significant visual information. Fig.1 shows the structure of a bit-plane. Fig.1 Bit-Plane representation of digital image [18] Bit-plane slicing provides three main goals [18] which are conversion of gray level image to a binary image, to represent an image in fewer bits and compression of image into smaller size, and lastly enhancing the image by focusing. Bit-plane information is useful in image coding especially in image compression [19]. Mathematically, bit-plane extraction can be derived from a gray-level image. Firstly, equation for gray-level is derived as in Equation (1). Then, the gray-level image is converted into binary, where f ( x, is divided into 2, so that the remainder is obtained for the feature of the specific bit-plane. It can be summarized that, the general derivation for bit-plane feature extraction is as shown in equation (2). f (0,0) f (1,0) f ( x, = f ( M 1,0) f f (0,1) f (1,1) f ( M 1,1) 1 1 ( x, = R floor i 2 2 [ f ( x, ] bp i ; f (0, N 1) f (1, N 1) f ( M 1, N 1) (1) i=0, 1, 2 7 (2) where f(x, is original image, fbp(x, is bit-plane information, R is the remainder, and floor(x) is ISBN:

3 round the elements to x nearest integers less than or equal to x [16]. 3 The Proposed Method Generally, there are four stages involves in the proposed palmprint recognition system. There are image acquisition, image pre-processing, bit-plane feature extraction and lastly verification stage by using Multilayer Perceptron (MLP) Neural Network. The processes of each stage are described in the following subsections. 3.1 Image Acquisition The database implied in this research is from the PolyU Palmprint Database [17]. This database consists of 7752 grayscale images corresponding to 386 different palms in bitmap image format. Approximately twenty samples from each of these palms were collected in two sessions. Around 10 of those samples were captured in the first session and the second session correspondingly. Also, the average interval between the first and the second collection was two months. Palm print images can be obtained in two different sizes, which are 384 x 284 and 768 x 568. The database applied for this project consists of images in size 384 x 284. Samples from the first session are used. It consists of 100 samples from ten sets of right hand palm prints where each set has ten samples of palmprint. Fig.2 Image database for PolyU Hong Kong Palmprint [17]. 3.2 Image Preprocessing into the neural network are not too large. Matlab provides embedded function to reduce the image s resolution. The Matlab syntax imresize is used to reduce image resolution to 64x64 pixels. After reducing the image resolution, enhancement is done on the images. The intensity level is adjusted to increase the contrast of the image output. Then, the histogram equalization is applied to the image. 3.3 Feature Extraction Using Bit-Level Information As proposed, the feature extraction employs the bitplane extraction method. In a gray level image of 8 bits level, it has 8 order bits with binary information. Bit-plane is therefore a group of binary information arranged according to bit order. All the least significant bits will form into bit-plane 0 while the most significant bits formed into bit-plane 7. In an original digital image, it has different level of the gray values in each pixel. Next, the gray value of each pixel is converted into binary value. Then, the values from the same bit-level of binaries are grouped together to form bit planes. Since 8-bit gray-level image is used, it formed 8 bit planes. All bit 0 are will grouped together to become bit-plane 0, and the same goes for the remaining bit-plane 1, 2, 3, 4, 5, 6, and Verification Stage A Multi Layer Perceptron Neural Network is proposed for the recognition stage. It is a feedforward artificial neural network model which categorizes sets of input data onto a set of appropriate output [21]. According to Fausett [22], there are two important stages in Perceptron Neural Network that are the learning stage and testing stage. The weight of networks is updated in learning stage while accuracy and reliability are evaluated in testing stage. The structure of MLP is illustrated in Fig. 3. Pre-processing is a stage where different palmprint images are aligned and its center is segmented for feature extraction [2]. Here, it is focus on the central region of interest (ROI). This is to isolate the central part of the palmprint which includes the texture and line information [20]. To obtain the ROI, the images are firstly cropped manually and saved into a gray scale images by using Adobe Photoshop. Next, the resolution is reduced to 64x64 pixels so that data fed ISBN:

4 Table 1. FAR and FRR for Single Bit-plane on Hong Kong PolyU Palmprint Database Bitplane FAR FRR HTER 4 36% 0% 18% Fig.3 Structure of MLP network [23]. 4 Results and Analysis We performed a single bit-plane extraction and analyzed its performance. In single bit-plane extraction, the performance is shown in Fig.4. The graph shows a comparison of recognition rate between single bit-planes and gray level image. The graph is plotted with overall percentage of performance for all training samples in each bitplane. Bit-0 until bit-3 are the lower order bit levels that show lower recognition rate, which is below 60%. On the other hand, bit-4 until bit-7 presents a higher performance. Although there is a drop of rate at bit-6, it still shows a higher recognition rate which is above 80%. Recognition Rate 100% 90% 80% 70% 60% 50% 40% 30% 20% Fig. 4 Recognition rates of single bit-planes in PolyU Hong Kong Palmprint 5 60% 0% 30% 6 18% 0% 9% 7 18% 0% 9% Table 2. FAR and FRR for Gray level image on Hong Kong PolyU Palmprint Database FAR FRR HTER Gray level image 16% 0% 8% FAR and FRR for the single bit-planes and gray level image are determined. The result is shown in Table 1 and Table 2. The FAR for bitplane 4 and 5 shows a rather high rate while bitplane 6 and 7 give 18% of error rate. As for gray level image, it is evaluated for benchmarking. It has a total of 16% FAR. However, no FRR is identified in both single bit-plane and gray level image during this experiment. Only 5 images are trained due to the limitation of database provided. Thus, the error rate can be reduced if more images are trained. 4 Conclusion A palmprint recognition system is designed and it consists of image acquaintance, pre-processing, bitplane feature extraction as significant input data and MLP neural network as classifier. The performance for the recognition system is evaluated and analyzed. The performance between single bitplanes and benchmarked gray-level images are compared successfully. The result shows that higher order of bit-level at the 5th, 6th and 7th bit planes is able to provide higher accuracy than a classical gray image input. Recognition rates are more than 80% for these bit planes. Therefore, it can be concluded that, bit-planes feature extraction is viable for palmprint recognition system. ISBN:

5 Acknowledgement The authors would like to acknowledge Ministry of Higher Education, Malaysia for the provision of research grant FRGS/03(03)/771/2010(52) and Faculty of Engineering, Universiti Malaysia Sarawak for the provision of research facilities. References: [1] NEC Automated Palmprint Identification roducts/ppi.html [2] Adams Wai-Kin Kong, David Dapeng Zhang, Mohamed S. Kamel: A survey of palmprint recognition. Pattern Recognition 42(7): (2009). [3] Edward Wong Kie yih, G. Sainarayanan, Ali Chekima, Palmprint Based Biometric System: A comparative study of Discrete Cosine Transform Energy, Wavelet Transform Energy, and SobelCode Methods, Biomedical Soft Computing and Human Sciences, Vol 14, No.1, pp , [4] C.C. Han, H.L. Cheng, C.L. Lin and K.C. Fan (2003): Personal Authentication using Palmprint features, Pattern Recognition, vol. 36, Issue 2, pp [5] K. Y. E. Wong, G. Sainarayanan and Ali Chekima (2006): Palmprint Authentication using Relative Geometric Features, 3rd International Conference on Artificial Intelligent and Engineering Technology (ICAIET 2006), pp [6] Feature Extraction. (n.d.). Retrieved March 29, 2012 from the Wikipedia: [7] Xiang-Qian Wu, Kuan-Quan Wang and David Zhang (2002): "Wavelet Based Palmprint Recognition", Proceedings of the First International Conference on Machine Learning and Cybernetics, Beijing, pp [8] Rigatos, G., Siano, P., Distributed state estimation for condition monitoring of nonlinear electric power systems", Industrial Electronics (ISIE), 2011 IEEE International Symposium on, On page(s): [9] X. Wang, H. Gong, H. Zhang, B. Li and Z. Zhuang, Palmprint identification using boosting local binary pattern, in Proceedings of International Conference on Pattern Recognition, pp , [10] R. Gonzalez and R. Woods, Digital Image Processing, Addison Wesley, [11] T. Ojala, Matti Pietikäinen, Topi Mäenpää, Multiresolution gray-sclae and rotation invariant texture classification with local binary patterns, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 24, no.7, pp , [12] Y. Freund and R.E. Schapire, A Decision- Theoretic Generalization of On-line Learning and An Application to Boosting, Journal of Computer and System Sciences, vol. 55, No. 1, pp , [13] Xiangqian Wu, Kuanquan Wang, and David Zhang (2006): Palmprint Texture Analysis Using Derivative of Gaussian Filters, International Conference on Computational Intelligence and Security 2006, Vol. 1, Issue:, Nov. 2006, pp [14] D. Zhang, W. K. Kong, J. You, M. Wong (2003): Online Palmprint Identification, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp [15] Xiao-Yuan Jing, David Zhang (2004): A face and palmprint recognition approaches based on discriminant DCT feature extraction, IEEE Transactions on Systems, Man, and Cybernetics, Part B 34(6), pp [16] D.B.L. Bong, K.C. Ting, and Y.C. Wang, Novel Face Recognition Approach Using Bit- Level Information and Dummy Blank Images in Feedforward Neural Network, Advances in Intelligent and Soft Computing, Vol. 58/2009, pp , [17] PolyU Palmprint Database, Polytechnic University of Hong Kong. [18] S. Jayaraman, S Esakkirajan, T Veerakumar. Image Enhancement, in Digital Image Processing, Tata McGraw-Hill, 2011, pp.275. [19] Majid Rabbani and Paul W. Melnychuck, "Conditioning Contexts for the Arithmetic Coding of Bit Planes," IEEE Trans. Communications, vol. 40, no. 1, pp , Jan [20] Saroj Kumar Panigrahy, A Secure Template Generation Scheme for Palmprint Recognition Systems, [21] Multiple Layer Perceptron Neural Network. (n.d.). Retrieved April 29, 2012 from the Wikipedia: on [22] Fausett, L., Fundamentals of Neural Networks. New Jersey:Prentice-Hall, Inc., [23] X. Wang, H. Gong, H. Zhang, B. Li and Z. Zhuang, Palmprint identification using boosting local binary pattern, in Proceedings of International Conference on Pattern Recognition, pp , ISBN: