TY  - BOOK
AU  - Liu,Chengjun
AU  - Mago,Vijay Kumar
ED  - SpringerLink (Online service)
TI  - Cross Disciplinary Biometric Systems
T2  - Intelligent Systems Reference Library,
SN  - 9783642284571
AV  - Q342 
U1  - 006.3 23
PY  - 2012///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Engineering
KW  - Artificial intelligence
KW  - Optical pattern recognition
KW  - Biometrics
KW  - Computational Intelligence
KW  - Pattern Recognition
KW  - Artificial Intelligence (incl. Robotics)
N1  - 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
N2  - 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
UR  - http://dx.doi.org/10.1007/978-3-642-28457-1
ER  -