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Deep learning-based face analytics

Chellappa, Rama.

Deep learning-based face analytics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning-based face analyticsedited by Nalini K Ratha, Vishal M. Patel, Rama Chellappa.
other author:	Ratha, Nalini K.
Published:	Cham :Springer International Publishing :2021.
Description:	vi, 407 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Human face recognition (Computer science)
Online resource:	https://doi.org/10.1007/978-3-030-74697-1
ISBN:	9783030746971

Deep learning-based face analytics
Deep learning-based face analytics[electronic resource] /edited by Nalini K Ratha, Vishal M. Patel, Rama Chellappa. - Cham :Springer International Publishing :2021. - vi, 407 p. :ill., digital ;24 cm. - Advances in computer vision and pattern recognition,2191-6594. - Advances in computer vision and pattern recognition..

1. Deep CNN Face Recognition: Looking at the Past and the Future -- 2. Face Segmentation, Face Swapping, and Their Effect on 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-Superresolution of Faces for Surveillance -- 7. Hashing a Face.

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.

ISBN: 9783030746971

Standard No.: 10.1007/978-3-030-74697-1doiSubjects--Topical Terms:

257587
Human face recognition (Computer science)

LC Class. No.: TA1653 / .D44 2021

Dewey Class. No.: 006.4

Deep learning-based face analytics
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505 0 $a 1. Deep CNN Face Recognition: Looking at the Past and the Future -- 2. Face Segmentation, Face Swapping, and Their Effect on 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-Superresolution of Faces for Surveillance -- 7. Hashing a Face.
520 $a 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.
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