Deep Learning for Biometrics

1. Front Matter

   Pages i-xxxi

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2. Deep Learning for Face Biometrics

   1. Front Matter

      Pages 1-1

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   2. 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

   3. Real-Time Face Identification via Multi-convolutional Neural Network and Boosted Hashing Forest

      • Yury Vizilter, Vladimir Gorbatsevich, Andrey Vorotnikov, Nikita Kostromov

      Pages 33-55

   4. CMS-RCNN: Contextual Multi-Scale Region-Based CNN for Unconstrained Face Detection

      • Chenchen Zhu, Yutong Zheng, Khoa Luu, Marios Savvides

      Pages 57-79

3. Deep Learning for Fingerprint, Fingervein and Iris Recognition

   1. Front Matter

      Pages 81-81

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   2. Latent Fingerprint Image Segmentation Using Deep Neural Network

      • Jude Ezeobiejesi, Bir Bhanu

      Pages 83-107

   3. Finger Vein Identification Using Convolutional Neural Network and Supervised Discrete Hashing

      • Cihui Xie, Ajay Kumar

      Pages 109-132

   4. Iris Segmentation Using Fully Convolutional Encoder–Decoder Networks

      • Ehsaneddin Jalilian, Andreas Uhl

      Pages 133-155

4. Deep Learning for Soft Biometrics

   1. Front Matter

      Pages 157-157

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   2. Two-Stream CNNs for Gesture-Based Verification and Identification: Learning User Style

      • Jonathan Wu, Jiawei Chen, Prakash Ishwar, Janusz Konrad

      Pages 159-182

   3. 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

   4. Gender Classification from NIR Iris Images Using Deep Learning

      • Juan Tapia, Carlos Aravena

      Pages 219-239

   5. Deep Learning for Tattoo Recognition

      • Xing Di, Vishal M. Patel

      Pages 241-256

5. Deep Learning for Biometrics Security and Protection

   1. Front Matter

      Pages 257-257

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   2. Learning Representations for Cryptographic Hash Based Face Template Protection

      • Rohit Kumar Pandey, Yingbo Zhou, Bhargava Urala Kota, Venu Govindaraju

      Pages 259-285

   3. Deep Triplet Embedding Representations for Liveness Detection

      • Federico Pala, Bir Bhanu

      Pages 287-307

6. Back Matter

   Pages 309-312

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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.

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 forgesture-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.

• Deep Learning
• Face
• Fingerprint
• Iris
• Gait
• Template Protection
• Anti-Spoofing
• Alexnet
• CNN
• RBM
• Biometrics
• Human Surveillance

“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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• University of California, Riverside, USA

  Bir Bhanu

• Hong Kong Polytechnic University, Hong Kong, China

  Ajay Kumar

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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