Cross disciplinary biometric systems
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...
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| Những tác giả chính: | , , |
|---|---|
| Định dạng: | Livre numérique |
| Ngôn ngữ: | Anglais |
| Được phát hành: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Phiên bản: | 1st ed. 2012. |
| Loạt: | Intelligent Systems Reference Library
37 |
| Truy cập trực tuyến: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Chú thích: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Cross Disciplinary Biometric Systems, Texte imprimé, 9783642284564 • Cross Disciplinary Biometric Systems, Texte imprimé, 9783642428401 • Cross Disciplinary Biometric Systems, Texte imprimé, 9783642284588 |
| LEADER | 04209nam a22003737a 4500 | ||
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| 001 | 958211 | ||
| 008 | 130325q2000 xx ||| |||| 00| 0 eng d | ||
| 009 | PPN168312034 | ||
| 020 | |a 9783642284571 | ||
| 041 | 0 | |a eng | |
| 082 | |a 006.3 | ||
| 100 | 1 | |a Liu, Chengjun, |d 19..-...., |c chercheur en informatique. | |
| 245 | 1 | 0 | |a Cross disciplinary biometric systems |c by Chengjun Liu, Vijay Kumar Mago. |
| 250 | |a 1st ed. 2012. | ||
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Intelligent Systems Reference Library |v 37 |x 1868-4408 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |a 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 | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a 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 | ||
| 700 | 1 | |a Mago, Vijay Kumar, |d 19..-...., |c chercheur en informatique. |4 aut | |
| 700 | 1 | |a Mago, Vijay Kumar. |4 aut | |
| 776 | 0 | |t Cross Disciplinary Biometric Systems |b Texte imprimé |z 9783642284564 | |
| 776 | 0 | |t Cross Disciplinary Biometric Systems |b Texte imprimé |z 9783642428401 | |
| 776 | 0 | |t Cross Disciplinary Biometric Systems |b Texte imprimé |z 9783642284588 | |
| 856 | 4 | |q PDF |u https://doi.org/10.1007/978-3-642-28457-1 |z Accès sur la plateforme de l'éditeur | |
| 856 | 4 | |u https://revue-sommaire.istex.fr/ark:/67375/8Q1-V3DXKX6Z-1 |z Accès sur la plateforme Istex | |
| 856 | 4 | |5 452349901:749564245 |u https://ezproxy.univ-orleans.fr/login?url=https://dx.doi.org/10.1007/978-3-642-28457-1 |z Accès Université d'Orléans | |
| 856 | 4 | |5 180339901:752313304 |u https://ezproxy.insa-cvl.fr/login?qurl=https://dx.doi.org/10.1007/978-3-642-28457-1 |z Accès INSA CVL | |
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