PERSONAL identification and verification both play an

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1 1 Multimodal Palmprint Biometric System using SPIHT and Radial Basis Function Djamel Samai 1, Abdallah Meraoumia 1, Salim Chitroub 2 and Noureddine Doghmane 3 1 Université Kasdi Merbah Ouargla, Laboratoire de Génie Électrique. Faculté des Nouvelles Technologies de l Information et de la Communication, Ouargla, 30000, Algérie 2 Signal and Image Processing Laboratory, Electronics and Computer Science Faculty, USTHB, P.O. box 32, El Alia, Bab Ezzouar, 16111, Algiers, Algeria 3 LASA Laboratory, Department of Electronics, Faculty of Engineering, Badji Mokhtar University, B.P. 12, Annaba, 23000, Algeria. samai.djamel@univ-ouargla.dz, ameraoumia@gmail.com, s chitroub@hotmail.com, ndoghmane@univ-annaba.org Abstract Palmprints have been widely studied for personal authentication because they are highly accurate. So, to minimize the amount of data to be transferred via a network link to the respective location with low bandwidth, and storage of reference data in template databases, we propose an efficient multimodal palmprint biometric system based on the famous Set Partitioning In Hierarchical Trees (SPIHT) coder and using the radial Basis Function (RBF). The fusion is made on matching score level. The proposed method is tested and evaluated on the PolyU palmprint database of 400 users. The experimental results show the effectiveness and reliability of the proposed approach, which brings both high identification and accuracy rate. Index Terms Biometrics, SPIHT, Multimodal Palmprint, RBF, Data fusion. I. INTRODUCTION PERSONAL identification and verification both play an important role in our life. Today, biometrics systems are used more and more in different activities and works. They replaced traditional knowledge-based or token-based personal identification or verification systems who became tedious time-consuming inefficient and expensive. These shortcomings have led to biometrics identification or verification systems becoming the focus of the research community in recent years [1][2]. Biometrics involves the automatic identification of an individual databases based on his physiological or behavioral characteristics template. In the literature, a number of biometric technologies have been proposed, and one of them is the hand-based biometrics, including fingerprint [3], palmprint [4][5], hand geometry or hand shape [6], hand vein [7], and Finger-Knuckle-Print (FKP) [8]. Their usage provides a reliable, low-cost and user-friendly viable solution for a range of access control applications. Palmprint identification is one kind of hand-biometric technology. The rich texture information of palmprint offers one of the powerful means in personal identification [4][9]. Palmprint contains many line features, for example, principal lines, wrinkles, and ridges. Because of the large surface and the rich line features that can be captured even with a lower resolution, we expect palmprints to be robust to noise and to have high individuality. For this reason, and due to the large amounts of data involved, we propose to use a compressed template storage of palmprints with low bitrate and to investigate the compression effects on 1 the recognition operation. Image compression is performed using the famous Set Partitioning In Hierarchical Trees (SPIHT) coder [10] and all experiments are done in pixel domain. In other words, the images are compressed to a certain bitrate and then uncompressed prior to use in recognition experiments. To improve our coding scheme for palmprint identification, we fuse the color palmprint image composed of three spectral bands (RGB) with the band Near-InfraRed (NIR) palmprint to generate a multimodal biometric system. The remainder of the paper is organized as follows. The proposed scheme for multimodal palmprint identification is exposed in section 2. Section 3 gives the proposed feature extraction method based on the SPIHT algorithm. The fusion technique used for fusing the information presented by extracted features is detailed in section 4. The experimental results prior to fusion and after fusion are given and commented in section 5. Finally, section 6 is devoted to the conclusion and future work. II. PROPOSED SYSTEM Fig. 1 illustrates the schematic diagram of the proposed system using multispectral palmprint images (RGB and NIR spectral bands). It is composed of two phases: an enrollment phase and an identification/verification phase. After a preprocessing operation to detect the key points of the palm, a feature extraction operation is necessary to obtain some effective features by using the SPIHT coder algorithm with low bitrate. We obtain a coarse version of palmprint images which preserve all their characteristics. The extracted feature vectors are stored in a set of observation vectors as reference templates after a training operation where we used a Radial Basis Function network [11]. Each RBF output will correspond to each class. For identification, an observation vector extracted from the test multispectral palmprint images and classifying them by the trained classifier. In the matching module which compare the input features and templates from the database, evaluation and maximum selection is used to measure the similarity between the vectors. Matching scores from both uni-biometric identification systems RGB and NIR are combined into a unique matching score using fusion at the matching-score level. Based on this unique matching score, a final decision of accepting or rejecting the user is then made.

2 2 Fig. 1. Multimodal palmprint identification/verification system based on SPIHT and Radial Basis Function. A. SPIHT algorithm III. FEATURE EXTRACTION The SPIHT algorithm [10] is an efficient algorithm for still image compression. Images decomposed by wavelet transform have self-similarity across levels. This similarity is exploited by SPIHT algorithm to establish Spatial Orientation Trees (SOT). Fig. 2 gives an example of SOT for a image with 2 levels [12]. The SPIHT algorithm orders the wavelet coefficients by magnitude and transmits them from the most significant bit (MSB) to the least significant bit (LSB). It partitions the coefficients or the sets of coefficients into significant or insignificant coefficients. Individual significant coefficients are added to the List of Significant Pixels (LSP), and insignificant coefficients to the List of Insignificant Pixels (LIP) while sets of descendant coefficients to the List of Insignificant Sets (LIS). When a LIS entry contains one or more significant pixels for a certain threshold value, it is partitioned into significant pixels, insignificant pixels, and insignificant sets. Whenever the algorithm determines the significance of a coefficient, it produces one bit for the information. The number of bits from significant tests is the same as the number of entries in the LIP and the LIS, and the number of sign bits produced corresponds the number of entries that are added to the LSP. Once a pixel enters the LIP, the pixel generates one bit for every bit plane to show whether it is significant or not. B. Palmprint feature representation Palmprint feature representation is to describe the features in a concise and easy to compare way. Therefore, we use the palmprint image after decompression with the SPIHT algorithm at low bitrate. Fig. 3 shows an example of palmprint image at 0.1 bits per pixel and 0.25 bits per pixel. If we work with the image at 0.25 bits per pixel, our proposed system is able to obtain features from a palm including principal lines wrinkles and ridge texture. The decompressed palmprint image is reordered to produce an one-dimensional vector for each image. So, the observation vectors for all persons have been modeled as RBF networks. A typical RBF network which is a model form a special neural network architecture contains three layers: input layer, hidden layer and output layer [11][13]. The neuron number of the input layer is decided by 2 Fig. 2. Spatial Orientation Trees in SPIHT. the feature vector dimension of samples, while the number of neurons in the hidden layer is adjustable. The neurons of the output layer are as many as the pattern classes. The values of the input variables (input vector) is forwarded from the input layer to the hidden layer. The nodes within each layer are fully connected to the previous layer nodes. The hidden nodes are characterized by the center locations and the nonlinear radial basis function they employ. Each hidden node receives the input vector, calculates the Euclidean distance between the center location and the input vector and finally performs a nonlinear transformation of the distance, using the radial basis function. The output of each hidden node is then multiplied by a particular weight, while the final output of the network is a simple summation of all the weighted hidden node activations. Conventionally, K-means clustering algorithm could be applied to find RBF centers which are the most important parameters [14]. IV. MATCHING, FUSION SCHEME AND DECISION The match score is a measure of similarity between the input and template biometric feature vectors. The top match can be determined by examining the match scores concerning all comparisons and reporting the identity of the template corresponding to the largest similarity score. Fusion is based on the combination of matching scores after separate feature extraction and comparison between reference data and test data

3 3 (a) (b) (c) Fig. 3. Reconstruction of (a) original Palmprint image encoded using (SPIHT) at (b) 0.1 bits per pixel and (c) 0.25 bits per pixel. for each subsystem. There are several matching score fusion rules integrate normalized matching scores of a user to produce the final matching score [15]. A. Simple Sum rule The Simple Sum rule takes the sum of the R matching scores of the k th user as the final matching score S k of this user. S k is calculated as follows: R S k = S ki (1) B. Product rule i=1 The Product rule regards the multiplication result of the R matching scores of the k th user R S k = S ki (2) C. Min Score rule i=1 The Min Score rule selects the minimum score from the R matching scores of the k th user as the final matching score of this user. This rule is expressed as follows: D. Max Score rule S k = min(s k1, S k2,..., S kr ) (3) The Max Score rule selects the maximum score from the R matching scores of the k th user as the final matching score of this user. This rule is shown as follows: E. Weighted Sum rule S k = max(s k1, S k2,..., S kr ) (4) The Weighted Sum rule assumes that the R biometric traits have different significance in personal authentication and assigns different weights to the matching scores of different traits. The weighted sum of the R matching scores, is considered as the final matching score of the k th user. This rule is shown as follows: R S k = w i S ki (5) i=1 The final result of the fusion is a new matching score, which is the basis for the classification decision of the entire system. 3 V. EXPERIMENTAL RESULTS AND DISCUSSION A. Experimental database Our experiments are designed for testing the accuracy and efficiency of the proposed method. They are performed on the multispectral palmprint database from the Hong Kong polytechnic university (PolyU) [16]. The database contains images captured with visible and infrared light. we have choose 400 different persons among 500. Each person contains 12 palms. With the total, we have 4800 palms. B. Evaluation Criterion We can measure the accuracy of any biometric recognition system by two values [14]. 1) The False Accept Rate (FAR): The FAR of an impostor n is defined as the number of accepted verification attempts for an impostor n by the number of all verification attempts for the same impostor n. The overall FAR for N imposters is defined as follows: F AR = 1 N F AR(n) (6) N n=1 2) The False Reject Rate (FRR): The FRR of a genuine user n is defined as the number of rejected verification attempts for a genuine user n by the number of all verification attempts for the same genuine user n. The overall FRR for N genuine users is defined as follows: F RR = 1 N F RR(n) (7) N n=1 FAR and FRR trade off against one another. The system threshold value is obtained based on the equal error rate (EER) criterion where F AR = F RR. In biometric system, we try to find both rates low as possible. Another performance measurement is obtained from FAR and FRR, which is called the Genuine Acceptance Rate (GAR). It represents the identification rate of the system. To visually depict the performance of a biometric system, the Receiver Operating Characteristic (ROC) curves are usually used. The ROC curves display how FAR changes with respect to the GAR and vice versa or FRR against FAR [15]. Biometric systems generate matching scores that represent how similar (or dissimilar) the input is compared with the stored template.

4 4 TABLE I EQUAL ERROR RATE AND ITS EQUIVALENT BITRATES. Bitrate EER RGB NIR TABLE II THE PERFORMANCE OF THE SYSTEM IDENTIFICATION UNDER DIFFERENT VALUES OF THRESHOLD. Palmprint T 0 FAR FRR GAR RGB compressed NIR RGB uncompressed NIR Fig. 4. Equal Error Rate against bitrate. C. Unimodal System Identification Test Results We begin our experiments by evaluating the system performance through each modality (RGB and NIR palmprints). We used 1200 training images (3 images for each person) and 3600 test images (9 images for each person) for each modality. We obtained 3600 genuine comparisons and impostor comparisons. Two identification modes occur, openset identification where the person is not guaranteed to exist in the database and the closed-set identification if the person is assumed to exist in the database. In our work, the proposed method was tested through the first mode test (open-set). First of all, our work is based on the compressed images, the bitrate is crucial, therefore, we must identify the best bitrate for our application. The used data is compressed with the SPIHT algorithm. We carried out several tests with different bitrates on the RGB and the NIR palmprints. Table 1 and Fig. 4 illustrate the Equal Error Rate (EER) against the bitrate. According to the preceding table and figure, it is interesting to notice that the best results was achieved at a low bitrate (0.25bpp). Thereafter, we will work with this bitrate. In order to illustrate the efficiency of using the compressed palmprint images, the obtained results at the given bitrate are compared to the results using original uncompressed images. Fig. 5a depicts the ROC curves which represent the performance measures of the open-set unimodal palmprint identification system for both experiments. Our identification system can achieve a best EER of % and % for a threshold T 0 = and T 0 = in the case of NIR and RGB compressed palmprints respectively. For the case of NIR and RGB uncompressed palmprints, we obtained EER of % and % for a threshold T 0 = and T 0 = Fig. 5b shows the ROC curves of GAR against FAR in the case of NIR and RGB palmprints for various thresholds and both cases (with/without compression). The results give a maximum GAR of % and % in the case of NIR and RGB compressed palmprints against a maximum GAR of % and % for the uncompressed palmprints. The performance of the system identification under different values of T 0, which control the FAR, the FRR and the GAR with percentage, is shown in Table 2. By looking at the results of Fig. 5, we can immediately conclude that compression at low bitrate (0.25bpp) does not significantly influence the identification results. Therefore, using compressed images exhibits some statistically significant improvements. SPIHT images seem to be visually less distorted at low bitrates and thus more appropriate for such uses. According to table 2, we note that when one increases the threshold of similarity, the FAR decreases and the FRR increases. That justifies well what one finds in the literature. D. Multimodal System Identification Test Results The NIR palmprint gave a good result, its insertion in the multimodal system by fusion with the RGB palmprint can produce a robust identification system with high accuracy and improve the rate of recognition. It promises to perform better than any one of its individual components (RGB or NIR). We made fusion at matching score which gives a good results. In our system, RGB and NIR images are fused with different combinations of fusion rules where they are tested to find the combination that optimizes the system accuracy. To find the better of the all fusion rules, with the lowest EER, table 3 illustrates the EER according to different values of thresholds for compressed and uncompressed palmprints. For example, if sum rule is used, we have EER = % and % for compressed and uncompressed palmprints respectively. In the case of using Product rule, EER was % and %. Using Min and Max rule, EER was % and % for compressed palmprints and % and % for uncompressed palmprints. A weighted rule improves the result and gives the best result (EER = % and % for both cases) for a database size equal to 400. Therefore, the system achieved higher accuracy at the fusion of the two matching score compared with a single matching score. Fig. 5 gives the graphs showing the ROC curves for various

5 5 (a) (b) Fig. 5. Unimodal identification ROC curves (a) FRR against FAR (b) GAR against FAR. (a) (b) Fig. 6. Multimodal identification ROC curves for weighted rule (a) FRR against FAR (b) GAR against FAR. TABLE III THE EER AGAINT THRESHOLDS FOR DIFFERENT FUSION RULES. Compressed palmprint Uncompressed palmprint Fusion rule EER T 0 EER T 0 Sum Product Min Max Weighted thresholds for the Weighted rule. The obtained results show that using the uncompressed palmprints offers better results in terms of EER and GAR. The obtained results show that the weighted rule offers better results in terms of the genuine acceptance rate. For example, if sum rule is used, we have EER = %. In the case of using Product rule, EER was %. Using Min and Max rule, EER was % and % respectively. A weighted rule improves the result (0.0002%) for a database size equal to 400. Therefore, the system can achieve higher accuracy at the fusion of the two matching score compared with a single matching score. VI. CONCLUSION AND FURTHER WORK The aim of this paper is to contribute to the multimodal identification by the use of data fusion rules. Two unimodal sub-systems derived from RGB and NIR spectrums were used in this study. Fusion of the two proposed unimodal sub-systems is performed at the matching score level to generate a fused matching score which is used for recognizing a palmprint image. Feature extraction process use SPIHT algorithm for images compression. It generate a template at

6 6 low bitrate that preserves the principal lines wrinkles and ridge texture. The experimental results, obtained on a database of 400 persons, show a very high open-set identification accuracy. In addition, our tests show that the multimodal system provides better open-set identification accuracy than the best unimodal systems. For further improvement, our future work will project to use other biometric modalities (Face and Iris) as well as the use of other fusion level like feature and decision levels. Also we will focus on the performance evaluation in both phases (verification and identification) by using a large size database. REFERENCES [1] David D. Zhang, Automated Biometrics Technologies and Systems, Originally published by Kluwer Academic Publishers, New York in [2] Anil K. Jain, Arun A. Ross, Karthik Nandakumar, Introduction to Biometrics, Springer Science+Business Media, LLC [3] D.Maltoni, D.Maio, A.K. Jain, S. Prabhakar, Handbook of Fingerprint Recognition, Springer, New York, June [4] David D. Zhang, Palmprint Authentication, Boston: Kluwer Academic Publishers, USA [5] A. Meraoumia, S. Chitroub, A. Bouridane, Do multispectral palmprint images be reliable for person identification?, Springer: Multimed Tools Appl vol. 74 pp: , [6] R. Zunkel, Hand geometry based verifications, in A. Jain, et al. (eds) Biometrics: Personal Identification in Networked Society. Kluwer Academic Press, [7] C. Wilson, Vein Pattern Recognition - A Privacy-Enhancing Biometric, Taylor and Francis Group, LLC, [8] Rui Zhao, Kunlun Li, Ming Liu, Xue Sun, A Novel Approach of Personal Identification Based on Single Knuckleprint Image, Asia- Pacific Conference on Information Processing, APCIP, [9] Ajay Kumar, David Zhang, Improving Biometric Authentication Performance From the User Quality, IEEE transactions on instrumentation and measurement, vol. 59, no. 3, march [10] A. Said, W. A. Pearlman, A new fast and efficient image codec based on set portioning in hierarchical trees, IEEE Trans. On Circuits and Systems for Video Technology, Vol. 6, pp , [11] Duda, R.O., Hart, P.E., Stork, D.G., Pattern Classification, 2nd edition. Wiley, New York [12] David Salomon, Data Compression, The Complete Reference, Fourth Edition, Springer-Verlag London Limited, [13] X. Jing, Y. Yao, D. Zhang, J. Yang, M. Li, Face and palmprint pixel level fusion and kernel dcv-rbf classifier for small sample biometric recognition, Pattern Recognition Vol. 40, no. 11, pp , [14] Min Han, Jianhui Xi, Efficient clustering of radial basis perceptron neural network for pattern recognition, Pattern Recognition Vol. 37, pp , [15] David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang, Advanced Pattern Recognition Technologies with Applications to Biometrics, Medical Information science reference, New York, [16] The Hong Kong Polytechnic University (PolyU) Multispectral Palmprint Database. Available at: biometrics/multispectralpalmprint/msp.htm. 6