Deep learning-based face analytics / Nalini K. Ratha, Vishal M. Patel, Rama Chellappa, editors.
Ratha, Nalini K. (Nalini Kanta), editor.; Patel, Vishal M., editor.; Chellappa, Rama, editor.
2021
TA1653 .D44 2021
Available Online

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Title Deep learning-based face analytics / Nalini K. Ratha, Vishal M. Patel, Rama Chellappa, editors.
ISBN 9783030746971 (electronic bk.)
3030746976 (electronic bk.)
9783030746964
3030746968
DOI https://doi.org/10.1007/978-3-030-74697-1
Published Cham : Springer, [2021]
Copyright ©2021
Language English
Description 1 online resource (vi, 407 pages) : illustrations (chiefly color)
Item Number 10.1007/978-3-030-74697-1 doi
Call Number TA1653 .D44 2021
Dewey Decimal Classification 006.4
Summary This book provides an overview of different deep learning-based methods for face recognition and related problems. Specifically, the authors present methods based on autoencoders, restricted Boltzmann machines, and deep convolutional neural networks for face detection, localization, tracking, recognition, etc. The authors also discuss merits and drawbacks of available approaches and identifies promising avenues of research in this rapidly evolving field. Even though there have been a number of different approaches proposed in the literature for face recognition based on deep learning methods, there is not a single book available in the literature that gives a complete overview of these methods. The proposed book captures the state of the art in face recognition using various deep learning methods, and it covers a variety of different topics related to face recognition. The prerequisites for optimal use are the basic knowledge of pattern recognition, machine learning, probability theory, and linear algebra. This book is aimed at graduate students studying electrical engineering and/or computer science. Biometrics is a course that is widely offered at both undergraduate and graduate levels at many institutions around the world: This book can be used as a textbook for teaching topics related to face recognition. In addition, the work is beneficial to practitioners in industry who are working on biometrics-related problems. Nalini K. Ratha is Empire Innovation professor in the Department of Computer Science and Engineering at University at Buffalo (New York). He is co-author and co-editor, respectively, of the Springer books, Guide to Biometrics and Advances in Biometrics. Vishal M. Patel is Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University (JHU). Rama Chellappa is Bloomberg Distinguished Professor in the Departments of Electrical and Computer Engineering and Biomedical Engineering at JHU. He is co-author and co-editor, respectively, of the Springer books, Unconstrained Face Recognition and Handbook of Remote Biometrics.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Digital File Characteristics text file
PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed August 25, 2021).
Added Author Ratha, Nalini K. (Nalini Kanta), editor.
Patel, Vishal M., editor.
Chellappa, Rama, editor.
Series Advances in computer vision and pattern recognition.
Available in Other Form Deep learning-based face analytics.
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All Resources
1. Deep CNN face recognition : looking at the past and the future
2. Face segmentation, face swapping, and how they impact face recognition
3. Disentangled representation learning and its application to face analytics
4. Learning 3D face morphable model from in-the-wild images
5. Deblurring face images using deep networks
6. Blind super-resolution of faces for surveillance
7. Hashing a face
8. Evolution of newborn face recognition
9. Deep feature fusion for face analytics
10. Deep learning for video face recognition
11. Thermal-to-visible face synthesis and recognition
Obstructing DeepFakes by disrupting face detection and facial landmarks extraction
Multi-channel face presentation attack detection using deep learning
Scalable person re-identification : beyond supervised approaches
Towards casual benchmarking of bias in face analysis algorithms
Strategies of face recognition by humans and machines
Evaluation of face recognition systems.

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