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Deep Learning for Biometrics

  • Bir Bhanu
  • Ajay Kumar

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

Table of contents

  1. Front Matter
    Pages i-xxxi
  2. Deep Learning for Face Biometrics

    1. Front Matter
      Pages 1-1
    2. Yury Vizilter, Vladimir Gorbatsevich, Andrey Vorotnikov, Nikita Kostromov
      Pages 33-55
    3. Chenchen Zhu, Yutong Zheng, Khoa Luu, Marios Savvides
      Pages 57-79
  3. Deep Learning for Fingerprint, Fingervein and Iris Recognition

  4. Deep Learning for Soft Biometrics

  5. Deep Learning for Biometrics Security and Protection

    1. Front Matter
      Pages 257-257
    2. Rohit Kumar Pandey, Yingbo Zhou, Bhargava Urala Kota, Venu Govindaraju
      Pages 259-285
    3. Federico Pala, Bir Bhanu
      Pages 287-307
  6. Back Matter
    Pages 309-312

About this book

Introduction

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.

    Keywords

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

    Editors and affiliations

    • Bir Bhanu
      • 1
    • Ajay Kumar
      • 2
    1. 1.University of CaliforniaRiversideUSA
    2. 2.Hong Kong Polytechnic UniversityHong KongChina

    Bibliographic information

    • DOI https://doi.org/10.1007/978-3-319-61657-5
    • Copyright Information Springer International Publishing AG, part of Springer Nature 2017
    • Publisher Name Springer, Cham
    • eBook Packages Computer Science
    • Print ISBN 978-3-319-61656-8
    • Online ISBN 978-3-319-61657-5
    • Series Print ISSN 2191-6586
    • Series Online ISSN 2191-6594
    • Buy this book on publisher's site
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