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001438987 003__ OCoLC
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001438987 020__ $$a9783030746971$$q(electronic bk.)
001438987 020__ $$a3030746976$$q(electronic bk.)
001438987 020__ $$z9783030746964
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001438987 0247_ $$a10.1007/978-3-030-74697-1$$2doi
001438987 035__ $$aSP(OCoLC)1264389262
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001438987 049__ $$aISEA
001438987 050_4 $$aTA1653$$b.D44 2021
001438987 08204 $$a006.4$$223
001438987 24500 $$aDeep learning-based face analytics /$$cNalini K. Ratha, Vishal M. Patel, Rama Chellappa, editors.
001438987 264_1 $$aCham :$$bSpringer,$$c[2021]
001438987 264_4 $$c©2021
001438987 300__ $$a1 online resource (vi, 407 pages) :$$billustrations (chiefly color)
001438987 336__ $$atext$$btxt$$2rdacontent
001438987 337__ $$acomputer$$bc$$2rdamedia
001438987 338__ $$aonline resource$$bcr$$2rdacarrier
001438987 347__ $$atext file
001438987 347__ $$bPDF
001438987 4901_ $$aAdvances in computer vision and pattern recognition
001438987 504__ $$aIncludes bibliographical references and index.
001438987 5050_ $$a1. Deep CNN face recognition : looking at the past and the future -- 2. Face segmentation, face swapping, and how they impact face recognition -- 3. Disentangled representation learning and its application to face analytics -- 4. Learning 3D face morphable model from in-the-wild images -- 5. Deblurring face images using deep networks -- 6. Blind super-resolution of faces for surveillance -- 7. Hashing a face -- 8. Evolution of newborn face recognition -- 9. Deep feature fusion for face analytics -- 10. Deep learning for video face recognition -- 11. Thermal-to-visible face synthesis and recognition -- Obstructing DeepFakes by disrupting face detection and facial landmarks extraction -- Multi-channel face presentation attack detection using deep learning -- Scalable person re-identification : beyond supervised approaches -- Towards casual benchmarking of bias in face analysis algorithms -- Strategies of face recognition by humans and machines -- Evaluation of face recognition systems.
001438987 506__ $$aAccess limited to authorized users.
001438987 520__ $$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.
001438987 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 25, 2021).
001438987 650_0 $$aHuman face recognition (Computer science)
001438987 650_0 $$aMachine learning.
001438987 650_6 $$aReconnaissance faciale (Informatique)
001438987 650_6 $$aApprentissage automatique.
001438987 655_0 $$aElectronic books.
001438987 7001_ $$aRatha, Nalini K.$$q(Nalini Kanta),$$eeditor.
001438987 7001_ $$aPatel, Vishal M.,$$eeditor.
001438987 7001_ $$aChellappa, Rama,$$eeditor.
001438987 77608 $$iPrint version:$$tDeep learning-based face analytics.$$dCham : Springer, [2021]$$z3030746968$$z9783030746964$$w(OCoLC)1243351309
001438987 830_0 $$aAdvances in computer vision and pattern recognition.
001438987 852__ $$bebk
001438987 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-74697-1$$zOnline Access$$91397441.1
001438987 909CO $$ooai:library.usi.edu:1438987$$pGLOBAL_SET
001438987 980__ $$aBIB
001438987 980__ $$aEBOOK
001438987 982__ $$aEbook
001438987 983__ $$aOnline
001438987 994__ $$a92$$bISE