Overview
- Authors:
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Chengjun Liu
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, Department of Computer Science, New Jersey Institute of Technology, Newark, USA
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Vijay Kumar Mago
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, The MoCSSy Program, Simon Fraser University, Burnaby, Canada
- Latest research in Cross Disciplinary Biometric Systems
- Includes applications to face recognition, iris recognition and fingerprint recognition
- Written by leading experts in the field
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Table of contents (10 chapters)
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- Zhiming Liu, Chengjun Liu
Pages 35-51
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- Zhiming Liu, Chengjun Liu
Pages 53-71
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- Peichung Shih, Chengjun Liu
Pages 93-116
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- Raffaele Cappelli, Matteo Ferrara, Davide Maltoni
Pages 117-150
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- Ruggero Donida Labati, Angelo Genovese, Vincenzo Piuri, Fabio Scotti
Pages 151-182
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- Sugata Banerji, Abhishek Verma, Chengjun Liu
Pages 205-225
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About this book
Cross disciplinary biometric systems help boost the performance of the conventional systems. Not only is the recognition accuracy significantly improved, but also the robustness of the systems is greatly enhanced in the challenging environments, such as varying illumination conditions. By leveraging the cross disciplinary technologies, face recognition systems, fingerprint recognition systems, iris recognition systems, as well as image search systems all benefit in terms of recognition performance. Take face recognition for an example, which is not only the most natural way human beings recognize the identity of each other, but also the least privacy-intrusive means because people show their face publicly every day. Face recognition systems display superb performance when they capitalize on the innovative ideas across color science, mathematics, and computer science (e.g., pattern recognition, machine learning, and image processing). The novel ideas lead to the development of new color models and effective color features in color science; innovative features from wavelets and statistics, and new kernel methods and novel kernel models in mathematics; new discriminant analysis frameworks, novel similarity measures, and new image analysis methods, such as fusing multiple image features from frequency domain, spatial domain, and color domain in computer science; as well as system design, new strategies for system integration, and different fusion strategies, such as the feature level fusion, decision level fusion, and new fusion strategies with novel similarity measures.
Authors and Affiliations
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, Department of Computer Science, New Jersey Institute of Technology, Newark, USA
Chengjun Liu
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, The MoCSSy Program, Simon Fraser University, Burnaby, Canada
Vijay Kumar Mago
