東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Deep learning-based face analytics

Ratha, Nalini K.

FindBook

Google Book

Amazon

博客來

Deep learning-based face analytics

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning-based face analytics/ edited by Nalini K Ratha, Vishal M. Patel, Rama Chellappa.
其他作者:	Ratha, Nalini K.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	vi, 407 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Human face recognition (Computer science) -
電子資源:	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:

704445
Human face recognition (Computer science)

LC Class. No.: TA1653 / .D44 2021

Dewey Class. No.: 006.4

Deep learning-based face analytics
LDR:03612nmm a2200349 a 4500 001 2249009
003 DE-He213
005 20210816114656.0
006 m d
007 cr nn 008maaau
008 220103s2021 sz s 0 eng d
020 $a 9783030746971 $q (electronic bk.)
020 $a 9783030746964 $q (paper)
024 7 $a 10.1007/978-3-030-74697-1 $2 doi
035 $a 978-3-030-74697-1
040 $a GP $c GP
041 0 $a eng
050 4 $a TA1653 $b .D44 2021
072 7 $a UYT $2 bicssc
072 7 $a COM012000 $2 bisacsh
072 7 $a UYT $2 thema
072 7 $a UYQV $2 thema
082 0 4 $a 006.4 $2 23
090 $a TA1653 $b .D311 2021
245 0 0 $a Deep learning-based face analytics $h [electronic resource] / $c edited by Nalini K Ratha, Vishal M. Patel, Rama Chellappa.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a vi, 407 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advances in computer vision and pattern recognition, $x 2191-6594
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.
650 0 $a Human face recognition (Computer science) $3 704445
650 0 $a Machine learning. $3 533906
650 1 4 $a Image Processing and Computer Vision. $3 891070
650 2 4 $a Biometrics. $3 898232
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 1619875
700 1 $a Ratha, Nalini K. $3 898875
700 1 $a Patel, Vishal M. $3 1672502
700 1 $a Chellappa, Rama. $3 1006256
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Advances in computer vision and pattern recognition. $3 1567575
856 4 0 $u https://doi.org/10.1007/978-3-030-74697-1
950 $a Computer Science (SpringerNature-11645)