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Optimal subset-division based discrimination and its kernelization for face and palmprint recognition. (English) Zbl 1242.68271
Summary: Discriminant analysis is effective in extracting discriminative features and reducing dimensionality. In this paper, we propose an optimal subset-division based discrimination (OSDD) approach to enhance the classification performance of discriminant analysis technique. OSDD first divides the sample set into several subsets by using an improved stability criterion and K-means algorithm. We separately calculate the optimal discriminant vectors from each subset. Then we construct the projection transformation by combining the discriminant vectors derived from all subsets. Furthermore, we provide a nonlinear extension of OSDD, that is, the optimal subset-division based kernel discrimination (OSKD) approach. It employs the kernel K-means algorithm to divide the sample set in the kernel space and obtains the nonlinear projection transformation. The proposed approaches are applied to face and palmprint recognition, and are examined using the AR and FERET face databases and the PolyU palmprint database. The experimental results demonstrate that the proposed approaches outperform several related linear and nonlinear discriminant analysis methods.

MSC:
 68T10 Pattern recognition, speech recognition 62H25 Factor analysis and principal components; correspondence analysis 62H30 Classification and discrimination; cluster analysis (statistical aspects)
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 [1] Fukunaga, K., Introduction to statistical pattern recognition, (1990), Academic Press, Inc. · Zbl 0711.62052 [2] Etemad, K.; Chellapa, R., Discriminant analysis for recognition of human face images, Journal of the optical society of America A, 14, 8, 1724-1733, (1997) [3] Mohanty, P.K.; Sarkar, S.; Phillips, P.J.; Kasturi, R., Subspace approximation of face recognition algorithms: an empirical study, IEEE transactions on information forensics and security, 3, 4, 734-748, (2008) [4] Kong, A.; Zhang, D.; Kamel, M., A survey of palmprint recognition, Pattern recognition, 42, 7, 1408-1418, (2009) [5] Belhumeur, P.N.; Hespanha, J.P.; Kriegman, D.J., Eigenfaces vs. fisherfaces: recognition using class specific linear projection, IEEE transactions on pattern analysis and machine intelligence, 19, 7, 711-720, (1997) [6] Yu, H.; Yang, J., A direct LDA algorithm for high-dimensional data with application to face recognition, Pattern recognition, 34, 10, 2067-2070, (2001) · Zbl 0993.68091 [7] Liu, C.J.; Wechsler, H., Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition, IEEE transactions on image processing, 11, 4, 467-476, (2002) [8] Dai, D.Q.; Yuen, P.C., Regularized discriminant analysis and its application to face recognition, Pattern recognition, 36, 3, 845-847, (2003) · Zbl 1032.68129 [9] Jing, X.Y.; Zhang, D.; Jin, Z., UODV: improved algorithm and generalized theory, Pattern recognition, 36, 11, 2593-2602, (2003) · Zbl 1059.68105 [10] Li, M.; Yuan, B.Z., 2D-LDA: a statistical linear discriminant analysis for image matrix, Pattern recognition letters, 26, 5, 527-532, (2005) [11] Ye, J.; Li, Q., A two-stage linear discriminant analysis via QR decomposition, IEEE transactions on pattern analysis and machine intelligence, 27, 6, 929-941, (2005) [12] Cevikalp, H.; Neamtu, M.; Wilkes, M.; Barkana, A., Discriminative common vectors for face recognition, IEEE transactions on pattern analysis and machine intelligence, 27, 1, 4-13, (2005) [13] Jing, X.Y.; Zhang, D.; Tang, Y.Y., An improved LDA approach, IEEE transactions on systems, man and cybernetics part B, 34, 5, 1942-1951, (2004) [14] T.K. Kim, S.F. Wong, B. Stenger, J. Kittler, R. Cipolla, Incremental linear discriminant analysis using sufficient spanning set approximations, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2007, pp. 1-8. [15] Y. Zhang, D.Y. Yeung, Semi-supervised discriminant analysis using robust path-based similarity, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008, pp. 1-8. [16] X.Y. Jing, S. Li, D. Zhang, J.Y. Yang, Face recognition based on local uncorrelated and weighted global uncorrelated discriminant transforms, in: Proceedings of the IEEE International Conference on Image Processing (ICIP 2011), 2011, pp. 3110-3113. [17] Tao, D.C.; Li, X.L.; Wu, X.D.; Maybank, S.J., Tensor rank one discriminant analysis— a convergent method for discriminative multilinear subspace selection, Neurocomputing, 71, 10-12, 1866-1882, (2008) [18] Zheng, W.S.; Lai, J.H.; Yuen, P.C.; Li, S.Z., Perturbation LDA: learning the difference between the class empirical Mean and its expectation, Pattern recognition, 42, 5, 764-779, (2009) · Zbl 1178.68523 [19] M. Zhu, A.M. Martinez, Optimal subclass discovery for discriminant analysis, in: Proceedings of the IEEE Workshop Learning in Computer Vision and Pattern Recognition (LCVPR), 2004, p. 97. [20] Zhu, M.; Martinez, A.M., Subclass discriminant analysis, IEEE transactions on pattern analysis and machine intelligence, 28, 8, 1274-1286, (2008) [21] M. Sugiyama, Local Fisher discriminant analysis for supervised dimensionality reduction, in: Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), Pittsburgh, PA, 2006, pp. 905-912. [22] M. Uray, P. Roth, H. Bischof, Efficient classification for large-scale problems by multiple LDA subspaces, in: Proceedings of the Fourth International Conference on Computer Vision Theory and Applications VISAPP, 2009, pp. 299-306. [23] X. Wang, X. Tang, Random sampling LDA for face recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, 2004, pp. 259-265. [24] Wang, X.; Tang, X., Random sampling for subspace face recognition, International journal of computer vision, 70, 1, 91-104, (2006) [25] S. Mika, G. Rätsch, J. Weston, B. Schölkopf, K.R. Müller, Fisher discriminant analysis with kernels, in: Proceeding of the IEEE Neural Networks for Signal Processing Workshop, 1999, pp. 41-48. [26] Baudat, G.; Anouar, F., Generalized discriminant analysis using a kernel approach, Neural computation, 12, 2385-2404, (2000) [27] Lu, J.; Plataniotis, K.N.; Venetsanopoulos, A.N., Face recognition using kernel direct discriminant analysis algorithms, IEEE transactions on neural networks, 14, 1, 117-126, (2003) [28] Liu, Q.S.; Lu, H.Q.; Ma, S.D., Improving kernel Fisher discriminant analysis for face recognition, IEEE transactions on circuits and systems for video technology, 14, 1, 42-49, (2004) [29] Yang, J.; Frangi, A.F.; Zhang, D.; Yang, J.Y.; Jin, Z., KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition, IEEE transactions on pattern analysis and machine intelligence, 27, 2, 230-244, (2005) [30] Jing, X.Y.; Yao, Y.F.; Zhang, D.; Yang, J.Y.; Li, M., Face and palmprint pixel level fusion and KDCV-RBF classifier for small sample biometric recognition, Pattern recognition, 40, 11, 3209-3224, (2007) · Zbl 1123.68361 [31] Chen, B.; Yuan, L.; Liu, H.; Bao, Z., Kernel subclass discriminant analysis, Neurocomputing, 72, 1-3, 455-458, (2007) [32] Sugiyama, M., Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis, Journal of machine learning research, 8, 1027-1061, (2007) · Zbl 1222.68312 [33] X.Y. Jing, S. Li, Y.F. Yao, L.S. Bian, J.Y. Yang, Kernel uncorrelated adjacent-class discriminant analysis, in: Proceedings of the International Conference on Pattern Recognition (ICPR), 2010, pp. 706-709. [34] S. Li, X.Y. Jing, D. Zhang, Y.F. Yao, L.S. Bian, A novel kernel discriminant feature extraction framework based on mapped virtual samples for face recognition, in: Proceedings of the IEEE International Conference on Image Processing (ICIP 2011), 2011, pp. 3066-3069. [35] Martinez, A.M.; Zhu, M., Where are linear feature extraction methods applicable?, IEEE transactions on pattern analysis and machine intelligence, 27, 12, 1934-1944, (2005) [36] Srivastava, H.M., Some simple algorithms for the evaluations and representations of the Riemann zeta function at positive integer arguments, Journal of mathematical analysis and applications, 246, 331-351, (2000) · Zbl 0957.11036 [37] Shawe-Taylor, J.; Cristianini, N., Kernel methods for pattern analysis, (2004), Cambridge University Press Cambridge [38] X. Tao, J. Ye, Q. Li, R. Janardan, V. Cherkassky, Efficient kernel discriminant analysis via QR decomposition. in: Proceedings of the Eighteenth Annual Conference on Neural Information Processing Systems (NIPS 2004), 2004, pp. 1529-1536. [39] A.M. Martinez, R. Benavente, The AR Face Database, CVC Technical Report 24, 1998. [40] Phillips, P.J.; Moon, H.; Rauss, P.; Rizvi, S.A., The FERET evaluation methodology for face recognition algorithms, IEEE transactions on pattern analysis and machine intelligence, 22, 10, 1090-1104, (2000) [41] Zhang, D.; Kong, W.K.; You, J.; Wong, M., On-line palmprint identification, IEEE transactions on pattern analysis and machine intelligence, 25, 9, 1041-1150, (2003)
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