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Discriminative Learning in Biometrics
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 the...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2016.
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| Edición: | 1st ed. 2016. |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 100 | 1 | |a Zhang, David. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Discriminative Learning in Biometrics |h [electronic resource] / |c by David Zhang, Yong Xu, Wangmeng Zuo. |
| 250 | |a 1st ed. 2016. | ||
| 264 | 1 | |a Singapore : |b Springer Nature Singapore : |b Imprint: Springer, |c 2016. | |
| 300 | |a XIII, 266 p. 110 illus., 73 illus. in color. |b online resource. | ||
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| 505 | 0 | |a 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. | |
| 520 | |a 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. . | ||
| 650 | 0 | |a Biometric identification. | |
| 650 | 0 | |a Pattern recognition systems. | |
| 650 | 1 | 4 | |a Biometrics. |
| 650 | 2 | 4 | |a Automated Pattern Recognition. |
| 700 | 1 | |a Xu, Yong. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Zuo, Wangmeng. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
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| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9789811020551 |
| 776 | 0 | 8 | |i Printed edition: |z 9789811020575 |
| 776 | 0 | 8 | |i Printed edition: |z 9789811095153 |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-981-10-2056-8 |z Texto Completo |
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| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||