Advanced pattern recognition technologies with applications by David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang

By David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang

With the expanding issues on safeguard breaches and transaction fraud, hugely trustworthy and handy own verification and identity applied sciences are a growing number of considered necessary in our social actions and nationwide providers. Biometrics, used to acknowledge the id of someone, are gaining ever-growing recognition in an in depth array of governmental, army, forensic, and advertisement safeguard functions.

Advanced development popularity applied sciences with purposes to Biometrics specializes in different types of complex biometric popularity applied sciences, biometric information discrimination and multi-biometrics, whereas systematically introducing fresh learn in constructing powerful biometric acceptance applied sciences. geared up into 3 major sections, this state of the art publication explores complicated biometric facts discrimination applied sciences, describes tensor-based biometric facts discrimination applied sciences, and develops the basic belief and different types of multi-biometrics applied sciences.

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Most recently, manifold learning methods, such as isometric feature mapping (ISOMAP), locally linear embedding (LLE), and Laplacian eigenmaps, have also shown great potential in biometric recognition (Tenenbaum, 2000; Roweis & Saul, 2000; Belkin & Niyogi, 2002). As a generalization of vector-based methods, a number of tensor discrimination technologies have been proposed. The beginning of tensor discrimination technology can be traced back to 1993, where a 2D image matrix based algebraic feature Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Wong, M. (2003). On-line palmprint identification. IEEE Transactions on Pattern Analysis Machine Intelligence, 25(9), 1041-1050. Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. , & Yang, Y. (1999). Theoretical analysis of illumination in PCA-based vision systems. Pattern Recognition, 32(4), 547-564. , & Weng, J. (1998). Discriminant analysis of principal components for face recognition. In H. J. Phillips, V. F. S.

Song, F. , Yang, J. , & Liu, S. H. (2004b). Pattern recognition based on the minimum norm minimum squared-error classifier. Proceedings of the Eighth International Conference on Control, Automation, Robotics and Vision (ICARCV 2004), (pp. 1114-1117). Song, F. , Chen, Q. , & Wang, J. Z. (2007). Face recognition based on a novel linear discriminant criterion. Pattern Analysis & Applications, 10(3), 165-174. , Gu, Z. , & Lee, S. H. (1986). Image classification by the Foley-Sammon transform. Optical Engineering, 25(7), 834-840.

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