Cross Disciplinary Biometric Systems

• Main Authors: Liu, Chengjun (Author), Mago, Vijay Kumar (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2012.
• Series: Intelligent Systems Reference Library, 37
• Subjects: Engineering.; Artificial intelligence.; Pattern recognition.; Biometrics (Biology).; Computational intelligence.; Computational Intelligence.; Biometrics.; Pattern Recognition.; Artificial Intelligence (incl. Robotics).

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...

Table of Contents:

• Feature Local Binary Patterns
• New Color Features for Pattern Recognition
• Gabor-DCT Features with Application to Face Recognition
• Frequency and Color Fusion for Face Verification
• Mixture of Classifiers for Face Recognition Across Pose
• Wavelet Features for 3D Face Recognition
• Minutiae-based Fingerprint Matching
• Iris segmentation: state of the art and innovative methods
• Various Discriminatory Features for Eye Detection
• LBP and Color Descriptors for Image Classification.