Deep Learning for Biometrics

 
 
Springer (Verlag)
  • erschienen am 12. Mai 2018
 
  • Buch
  • |
  • Softcover
  • |
  • XXXI, 312 Seiten
978-3-319-87128-8 (ISBN)
 
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.

Topics and features: 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.

Softcover reprint of the original 1st ed. 2017
  • Englisch
  • Cham
  • |
  • Schweiz
Springer International Publishing
  • Für Beruf und Forschung
  • 96
  • |
  • 97 farbige Tabellen, 21 s/w Abbildungen, 96 farbige Abbildungen
  • |
  • 97 Tables, color; 96 Illustrations, color; 21 Illustrations, black and white; XXXI, 312 p. 117 illus., 96 illus. in color.
  • Höhe: 23.5 cm
  • |
  • Breite: 15.5 cm
  • 5838 gr
978-3-319-87128-8 (9783319871288)
10.1007/978-3-319-61657-5
weitere Ausgaben werden ermittelt

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.

Part I: Deep Learning for Face Biometrics

The Functional Neuroanatomy of Face Processing: Insights from Neuroimaging and Implications for Deep Learning
Kalanit Grill-Spector, Kendrick Kay and Kevin S. Weiner

Real-Time Face Identification via Multi-Convolutional Neural Network and Boosted Hashing Forest
Yuri Vizilter, Vladimir Gorbatsevich, Andrey Vorotnikov and Nikita Kostromov

CMS-RCNN: Contextual Multi-Scale Region-Based CNN for Unconstrained Face Detection
Chenchen Zhu, Yutong Zheng, Khoa Luu and Marios Savvides

Part II: Deep Learning for Fingerprint, Fingervein and Iris Recognition

Latent Fingerprint Image Segmentation Using Deep Neural Networks
Jude Ezeobiejesi and Bir Bhanu

Finger Vein Identification Using Convolutional Neural Network and Supervised Discrete Hashing
Cihui Xie and Ajay Kumar

Iris Segmentation Using Fully Convolutional Encoder-Decoder Networks
Ehsaneddin Jalilian and Andreas Uhl

Part III: Deep Learning for Soft Biometrics

Two-Stream CNNs for Gesture-Based Verification and Identification: Learning User Style
Jonathan Wu, Jiawei Chen, Prakash Ishwar and Janusz Konrad

DeepGender2: A Generative Approach Toward Occlusion and Low Resolution Robust Facial Gender Classification via Progressively Trained Attention Shift Convolutional Neural Networks (PTAS-CNN) and Deep Convolutional Generative Adversarial Networks (DCGAN)
Felix Juefei-Xu, Eshan Verma and Marios Savvides

Gender Classification from NIR Iris Images Using Deep Learning
Juan Tapia and Carlos Aravena

Deep Learning for Tattoo Recognition
Xing Di and Vishal M. Patel

Part IV: Deep Learning for Biometric Security and Protection

Learning Representations for Cryptographic Hash Based Face Template Protection
Rohit Kumar Pandey, Yingbo Zhou, Bhargava Urala Kota and Venu Govindaraju

Deep Triplet Embedding Representations for Liveness Detection
Federico Pala and Bir Bhanu

"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)




 

"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)



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.

Topics and features:

- 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.
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.

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