Deep learning for biometrics /

Deep learning for biometrics /

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Bibliographic Details
Imprint:Cham : Springer, 2017.
Description:1 online resource (329 pages)
Language:English
Series:Advances in Computer Vision and Pattern Recognition
Advances in computer vision and pattern recognition.
Subject:
Biometric identification.
Machine learning.
Biometric identification.
Machine learning.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11349852
Hidden Bibliographic Details
Other authors / contributors:Bhanu, Bir.
Kumar, Ajay.
ISBN:9783319616575
3319616579
3319616560
9783319616568
Digital file characteristics:text file
PDF
Notes:Deep Learning for Fingerprint, Fingervein and Iris Recognition.
Includes bibliographical references at the end of each chapters and index.
Print version record.
Summary: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.
Other form:Print version: Bhanu, Bir. Deep Learning for Biometrics. Cham : Springer International Publishing, ©2017 9783319616568
Standard no.:10.1007/978-3-319-61657-5
Table of Contents:
  • Preface; Outline of the Book and Chapter Synopsis; Challenges for the Future; Acknowledgements; Contents; List of Figures; List of Tables; Deep Learning for Face Biometrics; 1 The Functional Neuroanatomy of Face Processing: Insights from Neuroimaging and Implications for Deep Learning; 1.1 The Functional Characteristics and Organization of the Ventral Face Network in the Human Brain; 1.1.1 Functional Characteristics of the Ventral Face Network; 1.2 The Neural Architecture and Connections of the Ventral Face Network.
  • 1.2.1 The Functional Organization of the Face Network Is Consistent Across Participants1.2.2 The Cytoarchitecture of Face-Selective Regions; 1.2.3 White Matter Connections of the Ventral Face Network; 1.3 Computations by Population Receptive Fields in the Ventral Face Network; 1.3.1 pRF Measurements Reveal a Hierarchical Organization of the Face Network; 1.3.2 Attention Modulates pRF Properties, Enhancing Peripheral Representations Where Visual Acuity Is the Worst; 1.4 Eyes to the Future: Computational Insights from Anatomical and Functional Features of the Face Network.
  • 1.4.1 What Is the Computational Utility of the Organized Structure of the Cortical Face Network?1.4.2 What Can Deep Convolutional Networks Inform About Computational Strategies of the Brain?; 1.5 Conclusions; References; 2 Real-Time Face Identification via Multi-convolutional Neural Network and Boosted Hashing Forest; 2.1 Introduction; 2.2 Related Work; 2.3 CNHF with Multiple Convolution CNN; 2.4 Learning Face Representation via Boosted Hashing Forest; 2.4.1 Boosted SSC, Forest Hashing and Boosted Hashing Forest; 2.4.2 BHF: Objective-Driven Recurrent Coding.
  • 2.4.3 BHF: Learning Elementary Projection via RANSAC Algorithm2.4.4 BHF: Boosted Hashing Forest; 2.4.5 BHF: Hashing Forest as a Metric Space; 2.4.6 BHF: Objective Function for Face Verification and Identification; 2.4.7 BHF Implementation for Learning Face Representation; 2.5 Experiments; 2.5.1 Methodology: Learning and Testing CNHF; 2.5.2 Hamming Embedding: CNHL Versus CNN, BHF Versus Boosted SSC; 2.5.3 CNHF: Performance w.r.t. Depth of Trees; 2.5.4 CNHL and CNHF Versus Best Methods on LFW; 2.6 Conclusion and Discussion; References.
  • 3 CMS-RCNN: Contextual Multi-Scale Region-Based CNN for Unconstrained Face Detection3.1 Introduction; 3.2 Related Work; 3.3 Background in Deep Convolution Nets; 3.3.1 Region-Based Convolution Neural Networks; 3.3.2 Limitations of Faster R-CNN; 3.3.3 Other Face Detection Method Limitations; 3.4 Contextual Multi-Scale R-CNN; 3.4.1 Identifying Tiny Faces; 3.4.2 Integrating Body Context; 3.4.3 Information Fusion; 3.4.4 Implementation Details; 3.5 Experiments; 3.5.1 Experiments on WIDER FACE Dataset; 3.5.2 Experiments on FDDB Face Database; 3.6 Conclusion and Future Work; References.

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