© 2012

Cross Disciplinary Biometric Systems

  • Latest research in Cross Disciplinary Biometric Systems

  • Includes applications to face recognition, iris recognition and fingerprint recognition

  • Written by leading experts in the field


Part of the Intelligent Systems Reference Library book series (ISRL, volume 37)

Table of contents

  1. Front Matter
    Pages 1-14
  2. Jiayu Gu, Chengjun Liu
    Pages 1-13
  3. Zhiming Liu, Chengjun Liu
    Pages 35-51
  4. Zhiming Liu, Chengjun Liu
    Pages 53-71
  5. Peichung Shih, Chengjun Liu
    Pages 93-116
  6. Raffaele Cappelli, Matteo Ferrara, Davide Maltoni
    Pages 117-150
  7. Ruggero Donida Labati, Angelo Genovese, Vincenzo Piuri, Fabio Scotti
    Pages 151-182
  8. Shuo Chen, Chengjun Liu
    Pages 183-203
  9. Sugata Banerji, Abhishek Verma, Chengjun Liu
    Pages 205-225
  10. Back Matter
    Pages 0--1

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.


Biometric Systems Face Recognition Fingerprint Recognition Intelligent Systems Iris Recognition

Authors and affiliations

  1. 1., Department of Computer ScienceNew Jersey Institute of TechnologyNewarkUSA
  2. 2., The MoCSSy ProgramSimon Fraser UniversityBurnabyCanada

Bibliographic information

  • Book Title Cross Disciplinary Biometric Systems
  • Authors Chengjun Liu
    Vijay Kumar Mago
  • Series Title Intelligent Systems Reference Library
  • DOI
  • Copyright Information Springe-Verlag Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Hardcover ISBN 978-3-642-28456-4
  • Softcover ISBN 978-3-642-42840-1
  • eBook ISBN 978-3-642-28457-1
  • Series ISSN 1868-4394
  • Series E-ISSN 1868-4408
  • Edition Number 1
  • Number of Pages XVI, 228
  • Number of Illustrations 0 b/w illustrations, 0 illustrations in colour
  • Topics Computational Intelligence
    Pattern Recognition
    Artificial Intelligence
  • Buy this book on publisher's site
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