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020 _a9783030746971
024 7 _a10.1007/978-3-030-74697-1
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aTA1653
_b2021 EB
245 0 0 _aDeep Learning-Based Face Analytics
_cedited by Nalini K Ratha, Vishal M. Patel, Rama Chellappa
250 _aFirst edition 2021
264 1 _aCham
_bSpringer International Publising
_c2021
300 _a1 recurso en línea (VI, 407 páginas)
_b182 ilustraciones, 169 ilustraciones a color
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _aarchivo de texto
_bPDF
490 0 _aAdvances in Computer Vision and Pattern Recognition
_x2191-6594
490 0 _aComputer Science (SpringerNature-11645)
490 0 _aComputer Science (R0) (SpringerNature-43710)
505 0 _a1. 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 3 _aThis 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.
988 _aSpringer_Computer_2021
650 7 _2embne
_aReconocimiento facial (Informática)
_9681178
650 7 _2embne
_aInteligencia artificial
_9413115
700 1 _aRatha, Nalini K
_eeditor literario
700 1 _aPatel, Vishal M
_eeditor literario
700 1 _aChellappa, Rama
_eeditor literario
776 0 8 _iPrinted edition:
_z9783030746964
776 0 8 _iPrinted edition:
_z9783030746988
776 0 8 _iPrinted edition:
_z9783030746995
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-74697-1
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
998 _b01/2022
_dz
_eu
_zSI