DSW Feature Based Hidden Marcov Model: An Application on Object Identification

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1 DSW Feature Based Hidden Marcov Model: An Application on Obect Identification Zheng Liang 1, Wang Taiqing 1, Wang Shengin 1 and Ding Xiaoqing 1 1 State Key Laboratory of Intelligent Technology and Systems 1 Department of Electronic Engineering, Tsinghua University, Beiing, , P.R.China zheng-l06@mails.tsinghua.edu.cn, wgsg@tsinghua.edu.cn Abstract This paper proposes to perform palmprint identification with Hidden Markov Models (HMM). Palmprint identification, as an emerging biometric technology, has been extensively investigated in the last decade. Due to its low-price capture device, fast implementation speed and high accuracy, palmprint identification is very competitive in biometric research area. Currently, the maority of literatures focus on palm line extraction algorithms and coding schemes, with little attention on classifier design. In this paper, Down-sliding Window (DSW) technique is employed to create a highcorrelated feature sequence while palmprint is featured by simple down-sampled images. One-to-50 experiment demonstrates that HMM with single component and six states give the best overall performance 99.80%, which indicates the feasibility of HMMs for tasks in palmprint identification. Keywords: palmprint identification; Hidden Markov Model; Down-SlidingWindow I. INTRODUCTION Palmprint identification has been arousing considerable amount of research interest in biometric field. This novel technique possesses pivotal qualities as a biometric system [1]: universality, distinctiveness, permanence, collectability. Compared with other biometric features, e.g., fingerprint, face and signature, palmprint is high in execution speed and performance, more discriminative, more acceptable, while requiring cheaper collection devices. Palmprint, the inner surface of palm, contains many unique features for recognition, such as principal lines, wrinkles, ridges, minutiae point and texture [2]. With the increasing interest in online biometric technology, lowresolution (less than 100 dpi) conditioned recognition has been a research focus. In this case, principal lines and wrinkles are most clearly observable features and a variety of research feature extraction methods have been proposed. In the texture-based methods, features are extracted by Gabor filter [3], the wavelet [4], or the ordinal filter [5]. In the palm line-based methods, the slope and intercept [6] are used. Meanwhile, coding-based approaches are among the most promising algorithms, such as PalmCode [7], Fusion Code [8], Competitive Code [6], etc. Nevertheless, the maority of recent work focuses on feature extraction and coding algorithms, with little, if any, attention on classifier design. Therefore, this paper is attempts to address the issue of palmprint identification from the point view of classifiers. Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states. HMM has been employed in various pattern recognition tasks such as face recognition [9], speech recognition [10], bioinformatics, etc. To the best of our knowledge, reference [11] is the only work attempting to address this issue by means of HMM. They report a continuous HMM, with Sobel operators followed by x-y proection as feature. A critical issue of HMM is to extract a highly correlated feature sequence, upon which a generative HMM model could be built. II. HIDDEN MARKOV MODEL Hidden Markov Model (HMM) is a statistical model in which assumes the system to be a Markov process whose states cannot be explicitly observed. For one hand, these states are related through a Markov process rather than independent of each other. For the other, each state has an associated probability distribution function, modeling the probability of emitting symbols from that state. Formally speaking, a discrete-time HMM could be defined as: N, M, A, B, (1) where N and M are number of states (denoted by q 1, q2,..., q N ) and number of different observation symbols per state (denoted by v 1, v2,..., vn ), respectively. The transition matrix A a i 1 i, N represents the probability of going from state q i to state q. The emission matrix B b k, 1 N, 1 k M denotes the emission probability of observation symbol v k at state q. i, 1 i Nstands for the initial state probability distribution in which 0 i 1is the initial probability of state qi and i i 1. This paper employ continuous HMM: the emission matrix is represented by mixture of finite Gaussian distribution, i.e /11/$26.00 c 2011 IEEE 502

2 M b ( O) c m1 where GO, and covariance.,,, 1 N G O (2), denotes a Gaussian density of mean In the model-training phase, given a set of observation sequences O i, standard Baum-Welch (a special case of EM algorithm) is employed to adust the model parameters to maximize P O. The parameter learning task is to find the best set of state transition and output probabilities. The training procedure is stopped after the convergence of the likelihood. The evaluation procedure, i.e. the computation of P O, given a model and a new observation sequence O, is performed using the forward-backward algorithm. This task is to compute the probability of a particular output sequence, which requires the summation over all possible state sequences. III. FEATURE EXTRACTION By adopting HMM for palmprint identification, it is of vital importance to extract features and rearrange them into a highly correlated observation sequence. For this, we employ the Down Sliding Window (DSW) algorithm first discussed by [13]. This sampling technique has been used in face recognition. The principle of this algorithm requires the target has some intrinsic fixed pattern in certain direction, such as face. Palmprint also has such quality, so it is feasible to transplant this method onto palmprint identification. As illustrated in Fig.1, a Down Sliding Window is employed to a YX image. A one-dimensional (1D) vector series of pixel observations O O1O 2... OT is generated, in which each observation Oi consists of the pixels in the LX block arranged into a column vector. Therefore, each observation vector is a block of L lines and there is a K-line overlap between successive observations. Thus, the length of the observation sequence O is given by: Y L T 1 (3) L K Assume that normalized palm images are placed as Fig.1, thus pixel blocks would appear in a predictable order. As a result, a left-right (non-ergodic) model could be built, in which only transitions between adacent states in a topbottom manner are allowed, as in Fig.2. Two stages are involved in HMM-based palmprint identification: training and testing. In the training procedure, a set of HMM parameters is trained on every palmprint category C i. Firstly, extract observation sequence of palmprint in C i. Secondly, from these sequences, compute HMM parameter i of this category by Baum-Welch algorithm. Finally, store HMM parameters of every class. Figure 1. DSW sampling technique Figure 2. State transition of an N-state left-right HMM In the testing procedure, first extract the observation vector of the palmprint under test. Then, for every HMM trained before, compute the probability of the presence of the current image in every HMM. Finally, find the maximum probability, and take the class index C i of the maximum value as recognition result. IV. EXPERIMENTAL RESULTS AND DISCUSSIONS All the experiments are performed on the Hong Kong PolyU palmprint database (version 2), which contains 7752 images captured from 193 individuals, 386 palms [12]. In this paper, for simplicity, we adopt a sub-database of palmprint of 50 random individuals. Fig.3 shows two examples from PolyU database. In section B, we employ images collected in the first session (10 images per person on average), in which a random 5 images are used for training, the rest for testing. In section C, D and E, we use all images (20 images per person on average), 10 for training (5 from session 1, 5 from session 2), the rest for testing. A. Preprocessing In palmprint recognition, preprocessing is a crucial step. The key point of preprocessing is to precisely locate reference points in the presence of transition and rotation during capture process. In this paper, the preprocessing procedure consists of six steps. (1) Low-pass Gaussian filter followed by binarization. (2) Morphological operations to eliminate noise. (3) Boundary tracking, then remove psudo-boundaries. (4) Locate two U-shaped areas between index and middle finger, ring and little finger. Find the common tangent of 2011 International Conference of Soft Computing and Pattern Recognition (SoCPaR) 503

3 these two areas, thus the tangency points are the critical points. (5) Build coordinate system and rotate the image. (6) Crop region-of-interest (ROI). The main preprocessing steps are illustrated in Fig.4. B. Find Optimal Number of States and Gaussian Mixtures First and foremost, this paper attempts to seek the optimal HMM states number N and Gaussian mixture number M. We assume N, M fall into the following range: 3 N 81 M 4 Experiment is performed with 50 randomly selected palmprint classes. For every class, we adopt those of the first collection session (10 on average), in which 5 was used for training, the rest for testing. We obtained 50 HMM parameters for new sample classification. The normalized images are 6464 in size, 8-bit gray scale. According to section III, every pixel block has a width of 64 pixels, height of 4 pixels, with step length of 4 pixels (0 overlap of adacent blocks). Therefore, parameters in Fig.1 are defined as: X 64 Y 64L 4K 0. Every individual, on average, generates 5 observations, with which a HMM with N states, M Gaussian mixtures is trained. The rest 5 images are used for testing. Experiment result is shown in Fig.5. From Fig.5, when state number N = 6, Gaussian mixture number M = 1, or N = 7, M = 1, identification rate exceeds 99%. Meanwhile, it is obvious that given state number N (except N = 4), single Gaussian mixture (dark blue bar) outperforms multi Gaussian mixtures in terms of recognition rate. This result indicates the efficiency of single Gauss function in describing data distribution. In the following experiments, we adopt M = 1, i.e. using single Gauss model to depict the distribution of continuous observation sequence. C. Varying Overlap K If there is no overlap between adacent pixel blocks, palmprint images would be partitioned into a serious of isolated, arbitrary observation sequences. In this case, some discriminating features might be cut out. In a top-bottom model without overlap, successful results depend on accurate alignment of feature sequences. However, in reality, due to some preprocessing error, feature areas are not the same for all images. On the other hand, allowing overlap during sampling may result in two functions: (1) Finer step length helps to avoid dropping important feature blocks. Therefore, larger overlap eases the effort to find feature area distribution. (2) Given palmprint image size and height of the pixel block, K determines the length of the observation sequence. Larger K results in longer observation sequence T, for palmprint is over-sampled. In fact, estimation of HMM parameters is closely related to the length of T. Smaller T means fewer occurrences of model events, which leads to worse estimation result, either is recognition accuracy. So it is of intuition that using a larger K would be advantageous. Figure 3. Two examples of PolyU database Figure 4. Palmprint preprocessing: (a) original image. (b) low-pass gaussian filter. (c) binarization. (d) morphological operations. (e) boundary tracking. (f) reference point location. (g) rotation. (h) cropping Figure 5. Recognition rate under different values of states number and Gaussian mixture number. To study the influence of overlap K on recognition accuracy, this paper first set N = 6 (according to section B). Then we varied the height of sampling window L, where 2 L 8, and 0 K L 1. Two representative results are shown in Fig.6. Fig.6 shows that, when overlap K increase, palmprint recognition error tends to decrease, which is consistent to initial expectation. But on the other hand, larger K results in larger T, and a time complexity of HMM is O (T ), so recognition time would increase linearly with K. Notably, under current parameter assumption (N = 6, M = 1, L = 4), when K = 3, recognition rate achieves 99.80%. D. Varying Window Height L The height of sliding window L mainly has 2 functions. First, L determines the feature dimension. Second, given International Conference of Soft Computing and Pattern Recognition (SoCPaR)

4 image size and overlap K, L determines the length of observation sequence T. To study the influence of window height on system performance, we consider two situations: no overlap (K = 0) and maximum overlap (K = N-1). We keep state number and Gaussian mixture number at N = 6 and M = 1, respectively. Intuitively, if L is small enough compared with image height Y, then the length of observation sequence T is large. Larger T helps to better estimate model parameter, but the difference between images would diminish due to smaller window height. As a result, the effect of L on recognition rate could be subtle. This is illustrated in Fig.7. It is obvious from Fig.7 that, when sampling window height L is small, pixel features are not discriminative enough to reflect the discrepancies between different samples. Therefore, when L = 1, recognition error is quite high. On the other hand, when we increase window height, the effect of L on error rate depends on overlap K. When K = 0, the number of observation sequence is small, and partitioning of palmprint image may be imprecise, so recognition error is relatively high. When K achieve its maximum value L-1, the number of observation sequence increases, and larger window height could adequately reflect the differences between images, so error rate is reduced. This may help to explain why the Fig.7(a) takes on U shape, while the Fig.7(b) tends to bend downward. E. Revisit to state number and Gaussian mixture number For a left-right HMM, the two parameters, state number and Gaussian mixture number, play an important role. State number determines the number of features needed to describe palmprint. If the length of observation sequence is large, we could select a larger state number to deal with the features. However, the forward-backward procedure requires 2 a time complexity of O ( N ), so smaller N corresponds to less computation time. To evaluate the effect of these two variables on the performance of palmprint identification, this paper attempts to experiment in more detail according to the results derived before. In section C, D, experiments are designed according to conclusion in section B, i.e. assuming N = 6 and M = 1 to be suitable. In this section, we would like to discuss how system performance would change with N and M. We adote L = 4, K = 3 (maximum pixel overlap), and assume 2 N 8, 1 M 3. Relationship between recognition rate and N, M is shown as Fig.8. From Fig.8(a), when M = 1, error rate is basically smaller than other values of M. If we focus on those results of M = 1, as in Fig.8(b), it is obvious that when N = 6, 7 and 8, error rate is quite small; but when N further decreases, error rate increases rapidly. When N = 6, M = 1, L = 4 and K = 3, the highest recognition rate 99.80% is achieved. Experiment result suggests that single Gaussian function could describe the data distribution quite well. In practical applications, if the system is required for real time processing, we could adopt a smaller state number N. Figure 6. Recognition error rate under different values of overlap K: (a) sliding window height L = 4. (b) L = 6. Figure 7. Recognition error rate under different values of sampling window height L: (a) no overlap. (b) maximum overlap. Figure 8. Recognition error rate under different values of state number and Gaussian mixture number: (a) variation of both state number and mixture number. (b) fix mixture number to 1. V. CONCLUSION Hidden Markov Model (HMM) has achieved remarkable performances in some pattern recognition fields. However, in the field of palmprint identification, study of the feasibility of HMM is limited. This paper employed Down Sliding Window (DSW) sampling technique to obtain observation sequences. In the experiment, this paper randomly selected 50 individuals, 1000 palmprint images from PolyU database, and discussed the influences of HMM and DSW parameters on system performance. Preliminary results suggest: (1) Hidden Markov Model is feasible for palmprint identification (with recognition precision of 99.80%). (2) Best performance is achieved when 6-states, singlemixture HMM is built, and DSW with window height 4, overlap International Conference of Soft Computing and Pattern Recognition (SoCPaR) 505

5 (3) Large overlap helps to improve performance. (4) When overlap is large, the height of sampling window has a marginal effect on recognition rate (when height is not too small). In the future work, some complex feature extraction method would be explored to for HMM based palmprint identification. Moreover, larger scale experiments would be performed to evaluate the scalability of the system. ACKNOWLEDGMENT This work was supported by the National Basic Research Program of China (973 program) under Grant No. 2007CB311004, the National Natural Science Foundation of China under Grant No and the Ph.D. Programs Foundation of Ministry of Education of China under Grant No REFERENCES [1] Anil K. Jain, Biometric Recognition: Overview and Recent Advances, Progress in Pattern Recognition, Image Analysis and Applications, vol. 4756, 2007, pp [2] A. Kong, D. Zhang and M. Kamel, A survey of palmprint recognition, Pattern Recognition, vol. 42, 2009, pp [3] D. Zhang, W. Kin Kong, J. You and M. Wong, Online Palmprint Identification, IEEE Transactions on Pattern Analysis and Machine Learning, vol. 25 (9), 2003, pp [4] X. Qian Wu, K. Quan Wang and D. Zhang, Wavelet Based Palmprint Recognition, Proc. of the ICMLC, vol. 3, 2002, pp [5] Z. Sun, T. Tan, Y. Wang and S. Z. Li, Ordinal palmprint representation for personal identification, IEEE Computer Society Conference on CVPR, vol. 1, 2005, pp [6] A. Kong, and D. Zhang, Competitive Coding Scheme for Palmprint Verification,Proc. of the ICPR, vol. 1, pp [7] D. Zhang, and W. Shu, Two Novel Characteristics in Palmprint Verification: Datum Point Invariance and Line Feature Matching, Pattern Recognition, vol. 32 (4), 1999, pp [8] A. Kong, D. Zhang and M. Kamel, Palmprint Identification Using Feature-level Fusion, Pattern Recognition, vol. 39 (3), 2006, pp [9] L. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proc. IEEE, vol. 77 (2), 1989, pp [10] A. V. Nefian, M. H. Hayes, An embeded HMM-based approach for face detection and recognition, Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 6,1999, pp [11] X. Wu, K. Wang and D. Zhang, HMMs based palmprint identification, Lecture Notes in Computer Science, Springer, vol. 3072, pp , [12] D. Zhang, PolyU Palmprint Database, Biometric Research Centre, Hong Kong Polytechnic University. Available from: ( [13] F. Samaria, Face segmentation for identification using hiden Markov models, British Machine Vision Conference, BMVA Press, 1993, pp International Conference of Soft Computing and Pattern Recognition (SoCPaR)