By David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang
With the expanding matters on safety breaches and transaction fraud, hugely trustworthy and handy own verification and identity applied sciences are a growing number of needful in our social actions and nationwide prone. Biometrics, used to acknowledge the id of somebody, are gaining ever-growing attractiveness in an intensive array of governmental, army, forensic, and advertisement defense purposes.
Advanced development reputation applied sciences with functions to Biometrics makes a speciality of different types of complex biometric reputation applied sciences, biometric information discrimination and multi-biometrics, whereas systematically introducing contemporary learn in constructing potent biometric popularity applied sciences. prepared into 3 major sections, this state-of-the-art booklet explores complex biometric information discrimination applied sciences, describes tensor-based biometric facts discrimination applied sciences, and develops the elemental belief and different types of multi-biometrics applied sciences.
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Additional info for Advanced pattern recognition technologies with applications to biometrics
In the experiment two kinds of original features: 256-dimensional Gabor transformation feature and 121-dimensional Legendre moment feature extracted by Hu, Lou, Yang, Liu and Sun, (1999) are used respectively. 5. Note that the results with asterisk are from (Yang, 2002). The accuracy of MNMSE is superior to all of face recognition techniques except for LSVM. The third experiment is performed on seven datasets available from the UCI machine learning repository (Murphy & Aha, 1992). 6. Statistical tests (t-test and rank-sum test) both show that there is no significant difference between Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
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. Proceedings of 18th International Conference on Machine Learning. San Francisco, CA, 306-313. , & Shi, P. (2005). An analytical method for generalized low rank approximation matrices. Pattern Recognition, 38(11), 2213-2216. , & Shi, P. (2007). The theoretical analysis of GRLDA and its applications.
The larger the score of between-class scatter is, the more separable the projected samples are; the smaller the score of within-class scatter is, the more separable the projected samples are. 26) T Minimize: w S w w . 27) There are mainly two ways to translate a multi-objective programming problem into a single-objective programming problem. The first is the goal programming approach in which one of the objectives is optimized while the remaining objectives are converted into constraints. The second is the combining objective approach in which all objectives are combined into one scalar objective (Eschenauer, Koski, & Osyczka, 1990).
Advanced pattern recognition technologies with applications to biometrics by David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang