Discriminative learning in biometrics [electronic resource] / David Zhang, Yong Xu, Wangmeng Zuo.
Zhang, David, 1949-; Xu, Yong.; Zuo, Wangmeng.
2016
QH323.5
Available Online

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Title Discriminative learning in biometrics [electronic resource] / David Zhang, Yong Xu, Wangmeng Zuo.
Author Zhang, David, 1949-
ISBN 9789811020568 (electronic book)
9811020566 (electronic book)
9789811020551
9811020558
Publication Details Singapore : Springer, c2016.
Language English
Description 1 online resource.
Call Number QH323.5
Dewey Decimal Classification 570.1/5195
Summary This monograph describes the latest advances in discriminative learning methods for biometric recognition. Specifically, it focuses on three representative categories of methods: sparse representation-based classification, metric learning, and discriminative feature representation, together with their applications in palmprint authentication, face recognition and multi-biometrics. The ideas, algorithms, experimental evaluation and underlying rationales are also provided for a better understanding of these methods. Lastly, it discusses several promising research directions in the field of discriminative biometric recognition. .
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Xu, Yong.
Zuo, Wangmeng.
Available in Other Form Print version: 9789811020551
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
1. Discriminative Learning in Biometrics
2. Metric Learning with Biometric Applications
3. Sparse Representation-based Classification for Biometric Recognition
4. Discriminative Features for Palmprint Authentication
5. Orientation Features and Distance Measure of Palmprint Authentication
6. Multifeature Palmprint Authentication
7. Discriminative Learning via Encouraging Virtual Face Images
8. Sparse Representation-based Methods for Face Recognition
9. Fusion Methodologies of Multiple Traits
10. Discussions and Future Work.

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