| Description |
1 online resource (xvi, 228 pages) |
| Series |
Intelligent systems reference library, 1868-4394 ; v. 37 |
|
Intelligent systems reference library ; v. 37.
|
| Contents |
Feature Local Binary Patterns / Jiayu Gu and Chengjun Liu -- New Color Features for Pattern Recognition / Chengjun Liu -- Gabor-DCT Features with Application to Face Recognition / Zhiming Liu and Chengjun Liu -- Frequency and Color Fusion for Face Verification / Zhiming Liu and Chengjun Liu -- Mixture of Classifiers for Face Recognition across Pose / Chengjun Liu -- Wavelet Features for 3D Face Recognition / Peichung Shih and Chengjun Liu -- Minutiae-Based Fingerprint Matching / Raffaele Cappelli, Matteo Ferrara and Davide Maltoni -- Iris Segmentation: State of the Art and Innovative Methods / Ruggero Donida Labati, Angelo Genovese, Vincenzo Piuri and Fabio Scotti -- Various Discriminatory Features for Eye Detection / Shuo Chen and Chengjun Liu -- LBP and Color Descriptors for Image Classification / Sugata Banerji, Abhishek Verma and Chengjun Liu |
| Summary |
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 |
| Analysis |
Engineering |
|
Artificial intelligence |
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Optical pattern recognition |
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Biometrics |
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Computational Intelligence |
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Pattern Recognition |
| Bibliography |
Includes bibliographical references and index |
| Notes |
English |
| Subject |
Human face recognition (Computer science)
|
|
Biometric identification.
|
|
COMPUTERS -- Computer Vision & Pattern Recognition.
|
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Ingénierie.
|
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Biometric identification
|
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Human face recognition (Computer science)
|
| Form |
Electronic book
|
| Author |
Mago, V. K.
|
| ISBN |
9783642284571 |
|
3642284574 |
|
3642284566 |
|
9783642284564 |
|
