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Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision

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Cross Disciplinary Biometric Systems (Paperback, 2012 ed.) Loot Price: R4,213
Discovery Miles 42 130
Cross Disciplinary Biometric Systems (Paperback, 2012 ed.): Chengjun Liu, Vijay Kumar Mago

Cross Disciplinary Biometric Systems (Paperback, 2012 ed.)

Chengjun Liu, Vijay Kumar Mago

Series: Intelligent Systems Reference Library, 37

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Loot Price R4,213 Discovery Miles 42 130 | Repayment Terms: R395 pm x 12*

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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.

General

Imprint: Springer-Verlag
Country of origin: Germany
Series: Intelligent Systems Reference Library, 37
Release date: May 2014
First published: 2012
Authors: Chengjun Liu • Vijay Kumar Mago
Dimensions: 235 x 155 x 13mm (L x W x T)
Format: Paperback
Pages: 228
Edition: 2012 ed.
ISBN-13: 978-3-642-42840-1
Categories: Books > Computing & IT > Applications of computing > Pattern recognition
Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision
LSN: 3-642-42840-1
Barcode: 9783642428401

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