Cross Disciplinary Biometric Systems

1. Front Matter

   Pages 1-14

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2. Feature Local Binary Patterns

   • Jiayu Gu, Chengjun Liu

   Pages 1-13

3. New Color Features for Pattern Recognition

   • Chengjun Liu

   Pages 15-34

4. Gabor-DCT Features with Application to Face Recognition

   • Zhiming Liu, Chengjun Liu

   Pages 35-51

5. Frequency and Color Fusion for Face Verification

   • Zhiming Liu, Chengjun Liu

   Pages 53-71

6. Mixture of Classifiers for Face Recognition across Pose

   • Chengjun Liu

   Pages 73-92

7. Wavelet Features for 3D Face Recognition

   • Peichung Shih, Chengjun Liu

   Pages 93-116

8. Minutiae-Based Fingerprint Matching

   • Raffaele Cappelli, Matteo Ferrara, Davide Maltoni

   Pages 117-150

9. Iris Segmentation: State of the Art and Innovative Methods

   • Ruggero Donida Labati, Angelo Genovese, Vincenzo Piuri, Fabio Scotti

   Pages 151-182

10. Various Discriminatory Features for Eye Detection

    • Shuo Chen, Chengjun Liu

    Pages 183-203

11. LBP and Color Descriptors for Image Classification

    • Sugata Banerji, Abhishek Verma, Chengjun Liu

    Pages 205-225

12. Back Matter

    Pages 0--1

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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

• , Department of Computer Science, New Jersey Institute of Technology, Newark, USA

  Chengjun Liu

• , The MoCSSy Program, Simon Fraser University, Burnaby, Canada

  Vijay Kumar Mago

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