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Biometric Authentication: A Machine Learning Approach
book

Biometric Authentication: A Machine Learning Approach

by S. Y. Kung, M. W. Mak, S. H. Lin
September 2004
Intermediate to advanced
496 pages
13h 57m
English
Pearson
Content preview from Biometric Authentication: A Machine Learning Approach

5. 1. Introduction

Neural networks have played a major role in many applications (e. g., see [171, 368]). In particular, they have demonstrated convincing performance in the detection and recognition of object classes. This is mainly due to their capability to cope with a variety of cues such as texture, intensity, edge, color and motion. In the context of personal identification, neural networks can facilitate detection or recognition of high-level features extracted from facial images or speakers' voices.

Supervised-learning network represent the mainstream of development in neural networks for biometric authentication. Some examples of well-known pioneering networks include the perceptron network [321], ADALINE/MADALINE [382], and various ...

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Publisher Resources

ISBN: 0131478249Purchase book