Normal View MARC View ISBD View

Cross Disciplinary Biometric Systems

by Liu, Chengjun.
Authors: Mago, Vijay Kumar.%author. | SpringerLink (Online service) Series: Intelligent Systems Reference Library, 1868-4394 ; . 37 Physical details: XVI, 228p. 112 illus., 58 illus. in color. online resource. ISBN: 3642284574 Subject(s): Engineering. | Artificial intelligence. | Optical pattern recognition. | Biometrics. | Engineering. | Computational Intelligence. | Biometrics. | Pattern Recognition. | Artificial Intelligence (incl. Robotics).
Tags from this library:
No tags from this library for this title.
Item type Location Call Number Status Date Due
E-Book E-Book AUM Main Library 006.3 (Browse Shelf) Not for loan

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.

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.

There are no comments for this item.

Log in to your account to post a comment.