By David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang
With the expanding matters on safeguard breaches and transaction fraud, hugely trustworthy and handy own verification and id applied sciences are increasingly more needful in our social actions and nationwide companies. Biometrics, used to acknowledge the identification of someone, are gaining ever-growing acceptance in an in depth array of governmental, army, forensic, and advertisement safeguard purposes.
Advanced trend popularity applied sciences with purposes to Biometrics specializes in types of complex biometric reputation applied sciences, biometric information discrimination and multi-biometrics, whereas systematically introducing fresh examine in constructing potent biometric attractiveness applied sciences. geared up into 3 major sections, this state of the art e-book explores complicated biometric info discrimination applied sciences, describes tensor-based biometric info discrimination applied sciences, and develops the basic belief and different types of multi-biometrics applied sciences.
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Additional resources for Advanced pattern recognition technologies with applications to biometrics
Guide to biometrics. New York: Springer-Verlag. , & Falavigna, D. (1995). Person identification using multiple cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(10), 955–966. Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. Overview 17 Cawley, G. , & Talbot, N. L. C. (2003). Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers. Pattern Recognition, 36(11), 2585-2592.
Belhumeur, P. , Hespanha, J. , & Kriengman, D. J. (1997). Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 711-720. , & Niyogi, P. (2002). Laplacian eigenmaps and spectral techniques for embedding and clustering. Advances in Neural Information Processing Systems, 14, 585-591. Billings, S. , & Lee, K. L. (2002). Nonlinear Fisher discriminant analysis using a minimum squared error cost function and the orthogonal least squares algorithm.
In the one-vs-rest strategy, an l-class problem is divided into l binary problems. In the ith problem a binary classifier is trained with all samples from the ith class with positive class labels, and all other samples with negative class labels. , l. An unknown sample x is assigned to the jth class if g j (x) = max gi (x). 1≤ i ≤ l In the one-vs-one strategy, an l-class problem is divided into l (l - 1) 2 binary problems: P(1, 2), …, P (i, j), …, P (l-1, l). In the problem P (i, j), a binary classifier is trained with all samples from the ith class with positive class labels, and all samples from the jth class with negative class labels.