Deep learning for biometrics

✍ Scribed by Bhanu, Bir; Kumar, Ajay

• Publisher: Springer
• Year: 2017
• Tongue: English
• Leaves: 329
• Series: Advances in computer vision and pattern recognition
• Category: Library

✦ Synopsis

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. Read more...

Abstract:

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; Read more...

✦ Table of Contents

Front Matter ....Pages i-xxxi
Front Matter ....Pages 1-1
The Functional Neuroanatomy of Face Processing: Insights from Neuroimaging and Implications for Deep Learning (Kalanit Grill-Spector, Kendrick Kay, Kevin S. Weiner)....Pages 3-31
Real-Time Face Identification via Multi-convolutional Neural Network and Boosted Hashing Forest (Yury Vizilter, Vladimir Gorbatsevich, Andrey Vorotnikov, Nikita Kostromov)....Pages 33-55
CMS-RCNN: Contextual Multi-Scale Region-Based CNN for Unconstrained Face Detection (Chenchen Zhu, Yutong Zheng, Khoa Luu, Marios Savvides)....Pages 57-79
Front Matter ....Pages 81-81
Latent Fingerprint Image Segmentation Using Deep Neural Network (Jude Ezeobiejesi, Bir Bhanu)....Pages 83-107
Finger Vein Identification Using Convolutional Neural Network and Supervised Discrete Hashing (Cihui Xie, Ajay Kumar)....Pages 109-132
Iris Segmentation Using Fully Convolutional Encoder–Decoder Networks (Ehsaneddin Jalilian, Andreas Uhl)....Pages 133-155
Front Matter ....Pages 157-157
Two-Stream CNNs for Gesture-Based Verification and Identification: Learning User Style (Jonathan Wu, Jiawei Chen, Prakash Ishwar, Janusz Konrad)....Pages 159-182
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, Marios Savvides)....Pages 183-218
Gender Classification from NIR Iris Images Using Deep Learning (Juan Tapia, Carlos Aravena)....Pages 219-239
Deep Learning for Tattoo Recognition (Xing Di, Vishal M. Patel)....Pages 241-256
Front Matter ....Pages 257-257
Learning Representations for Cryptographic Hash Based Face Template Protection (Rohit Kumar Pandey, Yingbo Zhou, Bhargava Urala Kota, Venu Govindaraju)....Pages 259-285
Deep Triplet Embedding Representations for Liveness Detection (Federico Pala, Bir Bhanu)....Pages 287-307
Back Matter ....Pages 309-312

✦ Subjects

Biometric identification;Machine learning;COMPUTERS / Computer Literacy;COMPUTERS / Computer Science;COMPUTERS / Data Processing;COMPUTERS / Hardware / General;COMPUTERS / Information Technology;COMPUTERS / Machine Theory;COMPUTERS / Reference;Computer Science;Artificial Intelligence (incl. Robotics);Biometrics;Mathematical Applications in Computer Science