Bioinformatics

Advanced pattern recognition technologies with applications - download pdf or read online

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

With the expanding matters on defense breaches and transaction fraud, hugely trustworthy and handy own verification and id applied sciences are progressively more needful in our social actions and nationwide prone. Biometrics, used to acknowledge the id of somebody, are gaining ever-growing acceptance in an in depth array of governmental, army, forensic, and advertisement defense functions.

Advanced trend popularity applied sciences with functions to Biometrics specializes in different types of complicated biometric popularity applied sciences, biometric information discrimination and multi-biometrics, whereas systematically introducing contemporary study in constructing potent biometric attractiveness applied sciences. geared up into 3 major sections, this state of the art publication explores complex biometric facts discrimination applied sciences, describes tensor-based biometric info discrimination applied sciences, and develops the basic notion and different types of multi-biometrics applied sciences.

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In biometric recognition, the data dimensionality is much higher than the size of the training set, leading to the well-known small sample size (SSS) problem. , 1998; Loog, Duin, & Haeb-Umbach, 2001; Duin & Loog, 2004; Ye, 2004; Howland & Park, 2004). The transform-based strategy first reduces the dimensions of the original image data and then uses LDA for feature extraction. Typical transform-based methods include PCA+LDA and uncorrected LDA. The algorithm-based strategy finds an algorithm for LDA that can circumvent the SSS problem.

Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. , Yang, J. , Hu, Z. , & Lou, Z. (2001). Face Recognition based on uncorrelated discriminant transformation. Pattern Recognition, 34(7), 1405-1416. , & Yang, J. Y. (2003). Face recognition based on a group decision-making combination approach. Pattern Recognition, 36(7), 1675-1678. Kim, H. , Bang, S. , & Lee, S. Y. (2004). Face recognition using the second-order mixture-of-eigenfaces method.

196-204).  (2000). Nonlinear discriminant analysis using kernel functions. Advances in Neural Information Processing Systems, 12, 568-574. Roweis, S. , & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290, 2323-2326. , & Muller, K. R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299-1319. , & Kirby, M. (1987). Low-dimensional procedure for characterization of human faces. Journal of Optical Society of America A, 4, 519-524.

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