LEADER 04117nam 2200517 450 001 996464392103316 005 20220504155642.0 010 $a3-030-74697-6 024 7 $a10.1007/978-3-030-74697-1 035 $a(CKB)4100000011999198 035 $a(DE-He213)978-3-030-74697-1 035 $a(MiAaPQ)EBC6705504 035 $a(Au-PeEL)EBL6705504 035 $a(PPN)257351345 035 $a(EXLCZ)994100000011999198 100 $a20220504d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aDeep learning-based face analytics /$fNalini K. Ratha, Vishal M. Patel, Rama Chellappa, editors 205 $a1st ed. 2021. 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (VI, 407 p. 182 illus., 169 illus. in color.) 225 1 $aAdvances in Computer Vision and Pattern Recognition,$x2191-6594 311 $a3-030-74696-8 327 $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. 330 $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. 410 0$aAdvances in computer vision and pattern recognition. 606 $aHuman face recognition (Computer science) 606 $aMachine learning 615 0$aHuman face recognition (Computer science) 615 0$aMachine learning. 676 $a006.37 702 $aRatha$b Nalini K. 702 $aPatel$b Vishal M. 702 $aChellappa$b Rama 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996464392103316 996 $aDeep Learning-Based Face Analytics$91907119 997 $aUNISA