By David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang
With the expanding issues on protection breaches and transaction fraud, hugely trustworthy and handy own verification and id applied sciences are a growing number of considered necessary in our social actions and nationwide companies. Biometrics, used to acknowledge the identification of someone, are gaining ever-growing recognition in an intensive array of governmental, army, forensic, and advertisement safety functions.
Advanced trend attractiveness applied sciences with purposes to Biometrics specializes in sorts of complex biometric popularity applied sciences, biometric info discrimination and multi-biometrics, whereas systematically introducing contemporary learn in constructing powerful biometric attractiveness applied sciences. prepared into 3 major sections, this state-of-the-art booklet explores complex biometric facts discrimination applied sciences, describes tensor-based biometric information discrimination applied sciences, and develops the elemental perception and different types of multi-biometrics applied sciences.
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Additional info for Advanced pattern recognition technologies with applications to biometrics
Sirovich, L. (1990). Application of the KL procedure for the characterization of human faces, IEEE Transactions on Pattern Analysis Machine Intelligence, 12(1), 103-108. , & Kamel, M. (2006). Palmprint identification using featurelevel fusion, Pattern Recognition, 39, 478-487. , & Vndewalle, J. (2000). A multilinear singular value decomposition. SIAM Journal on Matrix Analysis and Application, 21(4), 1233-1278. Lawrence, N. , & Schölkopf, B. (2001). Estimating a kernel Fisher discriminant in the presence of label noise.
The class of new methods includes: • • • • • • Extensions of Fisher’s Discriminant Criterion: We present three classification-oriented extensions of Fisher’s discriminant criterion: large margin linear projection (LMLP), minimum norm minimum squared-error, and maximum scatter difference (MSD). All these three criteria are designed to deal with the small sample size problem; Orthogonal Discriminant Analysis: We investigate two novel orthogonal discriminant analysis methods: orthogonalized Fisher discriminant (OFD) and Fisher discriminant with Schur decomposition (FDS).
IEEE Transactions on System, Man, and Cybernetics, Part A, 32(6), 787-793. , Kong, W. , & 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.