Get Advanced Topics In Biometrics PDF

By Haizhou Li, Kar-Ann Toh, Liyuan Li

ISBN-10: 9814287849

ISBN-13: 9789814287845

Biometrics is the learn of tools for uniquely spotting people in line with a number of intrinsic actual or behavioral qualities. After a long time of analysis actions, biometrics, as a famous clinical self-discipline, has complicated significantly either in functional expertise and theoretical discovery to satisfy the expanding desire of biometric deployments. during this ebook, the editors offer either a concise and obtainable advent to the sphere in addition to an in depth assurance at the particular study issues of their recommendations in a large spectrum of biometrics study starting from voice, face, fingerprint, iris, handwriting, human habit to multimodal biometrics. The contributions additionally current the pioneering efforts and cutting-edge effects, with targeted specialize in useful matters bearing on process improvement. This publication is a helpful reference for proven researchers and it additionally provides a superb advent for novices to appreciate the demanding situations.

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Haizhou Li, Kar-Ann Toh, Liyuan Li's Advanced Topics In Biometrics PDF

Biometrics is the examine of equipment for uniquely spotting people in keeping with a number of intrinsic actual or behavioral features. After a long time of study actions, biometrics, as a famous medical self-discipline, has complex significantly either in sensible know-how and theoretical discovery to fulfill the expanding desire of biometric deployments.

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2 Support vector machines The SVM is a technique for pattern classification, effective especially for highdimensional feature vectors. It is optimized based on a structural risk minimization principle (Vapnik, 1995). Because of its distribution-free property, it is suitable for designing vector-based classifiers. 14) i=1 where yi = {−1, 1} are the ideal outputs, the vectors xi are support vectors, N is the number of support vectors, the function K(·, ·) is the kernel, ai is an adjustable N weight, and b is a bias.

And Dai, L. Exploiting prosodic information for speaker recognition, in Proc. ICASSP, pp. 4225–4228, 2009. Lucey, S. and Chen, T. Improved speaker verification through probabilistic subspace adaptation, in Proc. Eurospeech, pp. 2021–2024, 2003. , and Li, H. Speaker cluster based GMM tokenization for speaker recognition, in Proc. Interspeech, pp. 505–508, 2006. Matsui, T. and Furui, S. Concatenated phoneme models for text-variable speaker recognition, in Proc. ICASSP, pp. 391–394, 1993. Naik, J.

In the last section, several common forms of variation in the speech signal are described in terms of feature modifications, as an illustration of the ease of explicitly describing variations that occur in speech using distinctive features. 2 The Distinctive Feature Representation of Speech Distinctive features describe a segment of speech, and can be thought of abstractly as being arranged in a column for a particular segment. Although there is some controversy over the exact list of distinctive features that describe speech, we adopt here a generally used set that is closely related to the articulatory processes that occur during speech production.

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Advanced Topics In Biometrics by Haizhou Li, Kar-Ann Toh, Liyuan Li


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