Screen LibraryDeep learning-based face analytics /
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, recogn...
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| Group Author: | ; ; |
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| Published: |
Springer,
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| Publisher Address: | Cham : |
| Publication Dates: | [2021] |
| Literature type: | Book |
| Language: | English |
| Series: |
Advances in computer vision and pattern recognition,
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| Subjects: | |
| 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-e |
| Carrier Form: | vi, 407 pages : illustrations (chiefly color) ; 25 cm. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
9783030746964 3030746968 |
| Index Number: | TA1653 |
| CLC: | TP391.41 |
| Call Number: | TP391.41/D311-4 |
| Contents: | 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. |