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Palmprint Identification Based On Probabilistic Rough Sets
[Full Text]
AUTHOR(S)
B.Lavanya, H. Hannah Inbarani
KEYWORDS
Palmprint, eigenpalm, rough sets, probability rough set,
ABSTRACT
Biometric-based identification is an emerging technology that can solve many security problems. Now-a-days, biometric palmprint has received wide attention from researchers because it is growing biometric feature for personal recognition. Palmprint presents the advantages like low resolution image, line features are stable, capturing device is low cost, and user-friendly. In this paper, palmprint biometric recognition method is proposed based on probabilistic rough set. Probability similarity between two palmprint image features is used for identification. One of the main advantages of probabilistic rough set in data analysis is that it does not need any preliminary information about data. Palmprint matching is mainly based on the feature representation of palmprint image. However, the palmprint images are transformed into a set of features called eigenpalms. These eigenpalm features are used for further processing of eigenpalm matching using probability rough set. The eigenpalm features are extracted using Principle components analysis(PCA). In Probabilistic Similarity Based rough set, this work has used all the features to define the Probabilistic similarity. Experimental results illustrate the effectiveness of the proposed model in terms of the recognition rate.
REFERENCES
[1]. Azam, N. and Yao, J., 2014. Analyzing uncertainties of probabilistic rough set regions with game-theoretic rough sets. International journal of approximate reasoning, 55(1), pp.142-155.
[2]. Badrinath, G.S. and Gupta, P., 2012. Palmprint based recognition system using phase-difference information. Future Generation Computer Systems, 28(1), pp.287-305.
[3]. CASIA Palmprint image database: http://biometrics.idealtest.org/
[4]. Chu, R., Lei, Z., Han, Y., He, R. and Li, S.Z., 2007, November. Learning Gabor magnitude features for palmprint recognition. In Asian Conference on Computer Vision (pp. 22-31). Springer, Berlin, Heidelberg.
[5]. Ciucci, D. and Forcati, I., 2017, July. Certainty-based rough sets. In International Joint Conference on Rough Sets (pp. 43-55). Springer, Cham.
[6]. COEP Palmprint Database: http://www.coep.org.in/resources/coeppalmprintdatabase
[7]. Dai, J., Feng, J. and Zhou, J., 2012. Robust and efficient ridge-based palmprint matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(8), pp.1618-1632.
[8]. Duta, N., 2009. A survey of biometric technology based on hand shape. Pattern Recognition, 42(11), pp.2797-2806.
[9]. Duta, N., Jain, A.K. and Mardia, K.V., 2002. Matching of palmprints. Pattern Recognition Letters, 23(4), pp.477-485.
[10]. Ekinci, M. and Aykut, M., 2007, July. Palmprint recognition by applying wavelet subband representation and kernel PCA. In International Workshop on Machine Learning and Data Mining in Pattern Recognition (pp. 628-642). Springer, Berlin, Heidelberg.
[11]. Fang, L., Leung, M.K., Shikhare, T., Chan, V. and Choon, K.F., 2006, October. Palmprint classification. In Systems, Man and Cybernetics, 2006. SMC'06. IEEE International Conference on (Vol. 4, pp. 2965-2969). IEEE.
[12]. Fei, L., Xu, Y., Tang, W. and Zhang, D., 2016. Double-orientation code and nonlinear matching scheme for palmprint recognition. Pattern Recognition, 49, pp.89-101.
[13]. IIT Delhi Palmprint image database: http://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Palm.htm
[14]. Kong, A., Zhang, D. and Kamel, M., 2006. Palmprint identification using feature-level fusion. Pattern Recognition, 39(3), pp.478-487.
[15]. Kong, A.K. and Zhang, D., 2004, August. Competitive coding scheme for palmprint verification. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on (Vol. 1, pp. 520-523). IEEE.
[16]. Kong, W.K. and Zhang, D., 2002. Palmprint texture analysis based on low-resolution images for personal authentication. In Pattern Recognition, 2002. Proceedings. 16th International Conference on (Vol. 3, pp. 807-810). IEEE.
[17]. Li, S.Z. and Jain, A., 2015. Encyclopedia of biometrics. Springer Publishing Company, Incorporated.
[18]. Li, W., Zhang, D. and Xu, Z., 2002. Palmprint identification by Fourier transform. International Journal of Pattern Recognition and Artificial Intelligence, 16(04), pp.417-432.
[19]. Lin, C.L., Chuang, T.C. and Fan, K.C., 2005. Palmprint verification using hierarchical decomposition. Pattern Recognition, 38(12), pp.2639-2652.
[20]. Lu, G., Zhang, D. and Wang, K., 2003. Palmprint recognition using eigenpalms features. Pattern Recognition Letters, 24(9-10), pp.1463-1467.
[21]. Lu, G.M., Wang, K.Q. and Zhang, D., 2004, August. Wavelet-based independent component analysis for palmprint identification. In Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference On (Vol. 6, pp. 3547-3550). IEEE.
[22]. Pawlak, Z., 1984. Rough probability. Bull. Pol. Acad. Sci. Math, 32, pp.607-615.
[23]. Pawlak, Z., Wong, S.K. and Ziarko, W., 1988. Rough sets: Probabilistic versus deterministic approach. International Journal of Man-Machine Studies.
[24]. Poon, C., Wong, D.C. and Shen, H.C., 2004, August. A new method in locating and segmenting palmprint into region-of-interest. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on (Vol. 4, pp. 533-536). IEEE.
[25]. Sun, Z., Tan, T., Wang, Y. and Li, S.Z., 2005, June. Ordinal palmprint representation for personal identification [represention read representation]. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (Vol. 1, pp. 279-284). IEEE.
[26]. Tiwari, K., Arya, D.K., Badrinath, G.S. and Gupta, P., 2013. Designing palmprint based recognition system using local structure tensor and force field transformation for human identification. Neurocomputing, 116, pp.222-230.
[27]. Van Nguyen, D., Yamada, K. and Unehara, M., 2013. Extended tolerance relation to define a new rough set model in incomplete information systems. Advances in Fuzzy Systems, 2013, p.9.
[28]. Wu, X., Zhang, D. and Wang, K., 2003. Fisherpalms based palmprint recognition. Pattern recognition letters, 24(15), pp.2829-2838.
[29]. Wu, X., Zhang, D., Wang, K. and Huang, B., 2004. Palmprint classification using principal lines. Pattern Recognition, 37(10), pp.1987-1998.
[30]. Yamada, K. and Unehara, M., 2013, October. On Probability of Matching in Probability Based Rough Set Definitions. In Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on (pp. 449-454). IEEE.
[31]. Yao, Y., 2008. Probabilistic rough set approximations. International journal of approximate reasoning, 49(2), pp.255-271.
[32]. Yao, Y., 2010. Three-way decisions with probabilistic rough sets. Information Sciences, 180(3), pp.341-353.
[33]. Yao, Y., 2011. Two semantic issues in a probabilistic rough set model. Fundamenta Informaticae, 108(3-4), pp.249-265.
[34]. Yao, Y., Greco, S. and Słowiński, R., 2015. Probabilistic rough sets. In Springer Handbook of Computational Intelligence (pp. 387-411). Springer, Berlin, Heidelberg.
[35]. Yao, Y.Y., 2003. Probabilistic approaches to rough sets. Expert systems, 20(5), pp.287-297.
[36]. Zhang, D., Kong, W.K., You, J. and Wong, M., 2003. Online palmprint identification. IEEE Transactions on pattern analysis and machine intelligence, 25(9), pp.1041-1050.
[37]. Zhang, L., Shen, Y., Li, H. and Lu, J., 2015. 3D palmprint identification using block-wise features and collaborative representation. IEEE transactions on pattern analysis and machine intelligence, 37(8), pp.1730-1736.
[38]. Zhang, S. and Liu, J., 2010, August. Palmprint recognition based on neighborhood rough set. In International Conference on Intelligent Computing (pp. 650-656). Springer, Berlin, Heidelberg.
[39]. Zhang, S., Wang, S. and Li, X., 2008, September. Palmprint linear feature extraction and identification based on ridgelet transforms and rough sets. In International Conference on Intelligent Computing (pp. 1101-1108). Springer, Berlin, Heidelberg.
[40]. Zhou, B. and Yao, Y., 2013, October. Comparison of two models of probabilistic rough sets. In International Conference on Rough Sets and Knowledge Technology (pp. 121-132). Springer, Berlin, Heidelberg.
[41]. Zhou, X., Peng, Y. and Yang, M., 2006, August. Palmprint recognition using wavelet and support vector machines. In Pacific Rim International Conference on Artificial Intelligence(pp. 385-393). Springer, Berlin, Heidelberg.
[42]. Ziarko, W., 2005, August. Probabilistic rough sets. In International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing (pp. 283-293). Springer, Berlin, Heidelberg.
[43]. Ziarko, W., 2008. A probabilistic approach to rough sets. International Journal of Approximate Reasoning, 49(2), pp.272-284.
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