Deep Learning for Biometrics / Advances in Computer Vision and Pattern Recognition (PDF)
(Sprache: Englisch)
This timely text/reference presents a broad overview of advanced deep learning architectures for learning effective feature representation for perceptual and biometrics-related tasks. The text offers a showcase of cutting-edge research on the use of...
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This timely text/reference presents a broad overview of advanced deep learning architectures for learning effective feature representation for perceptual and biometrics-related tasks. The text offers a showcase of cutting-edge research on the use of convolutional neural networks (CNN) in face, iris, fingerprint, and vascular biometric systems, in addition to surveillance systems that use soft biometrics. Issues of biometrics security are also examined.
Addresses the application of deep learning to enhance the performance of biometrics identification across a wide range of different biometrics modalities
Revisits deep learning for face biometrics, offering insights from neuroimaging, and provides comparison with popular CNN-based architectures for face recognition
Examines deep learning for state-of-the-art latent fingerprint and finger-vein recognition, as well as iris recognition
Discusses deep learning for soft biometrics, including approaches for gesture-based identification, gender classification, and tattoo recognition
Investigates deep learning for biometrics security, covering biometrics template protection methods, and liveness detection to protect against fake biometrics samples
Presents contributions from a global selection of pre-eminent experts in the field representing academia, industry and government laboratories
Providing both an accessible introduction to the practical applications of deep learning in biometrics, and a comprehensive coverage of the entire spectrum of biometric modalities, this authoritative volume will be of great interest to all researchers, practitioners and students involved in related areas of computer vision, pattern recognition and machine learning.
Topics and features:
Providing both an accessible introduction to the practical applications of deep learning in biometrics, and a comprehensive coverage of the entire spectrum of biometric modalities, this authoritative volume will be of great interest to all researchers, practitioners and students involved in related areas of computer vision, pattern recognition and machine learning.
Dr. Bir Bhanu is Bourns Presidential Chair, DistinguishedProfessor of Electrical and Computer Engineering and the Director of the Center for Research in Intelligent Systems at the University of California at Riverside, USA. Some of his other Springer publications include the titles Video Bioinformatics, Distributed Video Sensor Networks, and Human Recognition at a Distance in Video. Dr. Ajay Kumar is an Associate Professor in the Department of Computing at the Hong Kong Polytechnic University.
Autoren-Porträt
Dr. Bir Bhanu is Bourns Presidential Chair, Distinguished Professor of Electrical and Computer Engineering and the Director of the Center for Research in Intelligent Systems at the University of California at Riverside, USA. Some of his other Springer publications include the titles Video Bioinformatics, Distributed Video Sensor Networks, and Human Recognition at a Distance in Video.Dr. Ajay Kumar is an Associate Professor in the Department of Computing at the Hong Kong Polytechnic University.
Bibliographische Angaben
- 2017, 1st ed. 2017, 312 Seiten, Englisch
- Herausgegeben: Bir Bhanu, Ajay Kumar
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3319616579
- ISBN-13: 9783319616575
- Erscheinungsdatum: 01.08.2017
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: PDF
- Größe: 14 MB
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- Vorlesefunktion
Sprache:
Englisch
Pressezitat
“This book, which covers different deep learning neural architectures for solving an extended set of problems in the area of biometrics, is sure to catch the attention of scholars and researchers working in the field.” (CK Raju, Computing Reviews, February, 2019)
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