Biometric authentication: a machine learning approach
Prentice Hall Professional Technical Reference, 2005 - Computers - 476 pages
A breakthrough approach to improving biometrics performance Constructing robust information processing systems for face and voice recognition Supporting high-performance data fusion in multimodal systems Algorithms, implementation techniques, and application examples Machine learning: driving significant improvements in biometric performance As they improve, biometric authentication systems are becoming increasingly indispensable for protecting life and property. This book introduces powerful machine learning techniques that significantly improve biometric performance in a broad spectrum of application domains. Three leading researchers bridge the gap between research, design, and deployment, introducing key algorithms as well as practical implementation techniques. They demonstrate how to construct robust information processing systems for biometric authentication in both face and voice recognition systems, and to support data fusion in multimodal systems. Coverage includes: How machine learning approaches differ from conventional template matching Theoretical pillars of machine learning for complex pattern recognition and classification Expectation-maximization (EM) algorithms and support vector machines (SVM) Multi-layer learning models and back-propagation (BP) algorithms Probabilistic decision-based neural networks (PDNNs) for face biometrics Flexible structural frameworks for incorporating machine learning subsystems in biometric applications Hierarchical mixture of experts and inter-class learning strategies based on class-based modular networks Multi-cue data fusion techniques that integrate face and voice recognition Application case studies
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BIOMETRIC AUTHENTICATION SYSTEMS
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A'-means adaptation antispeaker applied approach basis function biometric authentication cepstral classifier cluster component computation covariance covariance matrix data points database DBNN decision boundary denotes density detection discriminant function distorted distribution error rate estimated example expert eye localizer face detector face recognition face recognition system facial images feature extraction feature vectors fusion fuzzy gating network handset selector hidden nodes hierarchical impostor scores input pattern iteration j-th kernel learning rule likelihood linear SVM linearly separable matrix method MFCCs MLLR modular neural networks neuron nonlinear obtained OCON optimal output parameters PDBNN face perceptron performance posterior probabilities prior score probabilistic RBF networks reinforced learning shown in Figure speaker models speaker recognition speaker verification structure subnet supervised learning support vector machines support vectors techniques template threshold training data training patterns training set transformation unseen handsets utterances weights XOR problem