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Deep Learning for Biometrics

  • Book
  • © 2017

Overview

Editors:
  1. Bir Bhanu

    1. University of California, Riverside, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Ajay Kumar

    1. Hong Kong Polytechnic University, Hong Kong, China

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • The first dedicated work on advances in biometric identification capabilities using deep learning techniques
  • Covers a broad range of deep learning integrated biometric techniques, including face, fingerprint, iris, gait, template protection, and issues of security
  • Provides overviews of basic deep learning and biometrics topics for novices in these fields, complete with references for further reading
  • Includes supplementary material: sn.pub/extras

Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR)

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Table of contents (12 chapters)

  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

  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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Keywords

About this book

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.

Reviews

“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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Editors and Affiliations

  • University of California, Riverside, USA

    Bir Bhanu

  • Hong Kong Polytechnic University, Hong Kong, China

    Ajay Kumar

About the editors

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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