Items related to Biometric Authentication: A Machine Learning Approach
Synopsis
A breakthrough approach to improving biometrics performanceConstructing robust information processing systems for face and voice recognitionSupporting high-performance data fusion in multimodal systemsAlgorithms, implementation techniques, and application examplesMachine learning: driving significant improvements in biometric performanceAs 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 recog
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About the Author
Man-Wai Mak is an assistant professor at The Hong Kong Polytechnic University and chairman of the IEEE Hong Kong Section Computer Chapter.
From the Back Cover
- 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
"About this title" may belong to another edition of this title.
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