國立虎尾科技大學 |

Deep learning for biometrics

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Deep learning for biometrics/ edited by Bir Bhanu, Ajay Kumar.
其他作者:	Bhanu, Bir.
出版者:	Cham :Springer International Publishing : : 2017.,
面頁冊數:	xxxi, 312 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Biometric identification. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-61657-5
ISBN:	9783319616575

Deep learning for biometrics
Deep learning for biometrics[electronic resource] /edited by Bir Bhanu, Ajay Kumar. - Cham :Springer International Publishing :2017. - xxxi, 312 p. :ill., digital ;24 cm. - Advances in computer vision and pattern recognition,2191-6586. - Advances in computer vision and pattern recognition..

Part I: Deep Learning for Face Biometrics -- The Functional Neuroanatomy of Face Processing: Insights from Neuroimaging and Implications for Deep Learning -- Real-Time Face Identification via Multi-Convolutional Neural Network and Boosted Hashing Forest -- CMS-RCNN: Contextual Multi-Scale Region-Based CNN for Unconstrained Face Detection -- Part II: Deep Learning for Fingerprint, Fingervein and Iris Recognition -- Latent Fingerprint Image Segmentation Using Deep Neural Networks -- Finger Vein Identification Using Convolutional Neural Network and Supervised Discrete Hashing -- Iris Segmentation Using Fully Convolutional Encoder-Decoder Networks -- Part III: Deep Learning for Soft Biometrics -- Two-Stream CNNs for Gesture-Based Verification and Identification: Learning User Style -- DeepGender2: A Generative Approach Toward Occlusion and Low Resolution Robust Facial Gender Classification via Progressively Trained Attention Shift Convolutional Neural Networks (PTAS-CNN) and Deep Convolutional Generative Adversarial Networks (DCGAN) -- Gender Classification from NIR Iris Images Using Deep Learning -- Deep Learning for Tattoo Recognition -- Part IV: Deep Learning for Biometric Security and Protection -- Learning Representations for Cryptographic Hash Based Face Template Protection -- Deep Triplet Embedding Representations for Liveness Detection.

This timely text/reference presents a broad overview of advanced deep learning architectures for learning effective feature representation for perceptual and biometrics-related tasks. The text offers a showcase of cutting-edge research on the use of convolutional neural networks (CNN) in face, iris, fingerprint, and vascular biometric systems, in addition to surveillance systems that use soft biometrics. Issues of biometrics security are also examined. Topics and features: Addresses the application of deep learning to enhance the performance of biometrics identification across a wide range of different biometrics modalities Revisits deep learning for face biometrics, offering insights from neuroimaging, and provides comparison with popular CNN-based architectures for face recognition Examines deep learning for state-of-the-art latent fingerprint and finger-vein recognition, as well as iris recognition Discusses deep learning for soft biometrics, including approaches for gesture-based identification, gender classification, and tattoo recognition Investigates deep learning for biometrics security, covering biometrics template protection methods, and liveness detection to protect against fake biometrics samples Presents contributions from a global selection of pre-eminent experts in the field representing academia, industry and government laboratories Providing both an accessible introduction to the practical applications of deep learning in biometrics, and a comprehensive coverage of the entire spectrum of biometric modalities, this authoritative volume will be of great interest to all researchers, practitioners and students involved in related areas of computer vision, pattern recognition and machine learning. Dr. Bir Bhanu is Bourns Presidential Chair, Distinguished Professor of Electrical and Computer Engineering and the Director of the Center for Research in Intelligent Systems at the University of California at Riverside, USA. Some of his other Springer publications include the titles Video Bioinformatics, Distributed Video Sensor Networks, and Human Recognition at a Distance in Video. Dr. Ajay Kumar is an Associate Professor in the Department of Computing at the Hong Kong Polytechnic University.

ISBN: 9783319616575

Standard No.: 10.1007/978-3-319-61657-5doiSubjects--Topical Terms:

598553
Biometric identification.

LC Class. No.: TK7882.B56

Dewey Class. No.: 006.248

Deep learning for biometrics
LDR:04630nam a2200337 a 4500 001 923451
003 DE-He213
005 20180313141907.0
006 m d
007 cr nn 008maaau
008 190625s2017 gw s 0 eng d
020 $a 9783319616575 $q (electronic bk.)
020 $a 9783319616568 $q (paper)
024 7 $a 10.1007/978-3-319-61657-5 $2 doi
035 $a 978-3-319-61657-5
040 $a GP $c GP
041 0 $a eng
050 4 $a TK7882.B56
072 7 $a UYQ $2 bicssc
072 7 $a TJFM1 $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.248 $2 23
090 $a TK7882.B56 $b D311 2017
245 0 0 $a Deep learning for biometrics $h [electronic resource] / $c edited by Bir Bhanu, Ajay Kumar.
260 $a Cham : $c 2017. $b Springer International Publishing : $b Imprint: Springer,
300 $a xxxi, 312 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advances in computer vision and pattern recognition, $x 2191-6586
505 0 $a Part I: Deep Learning for Face Biometrics -- The Functional Neuroanatomy of Face Processing: Insights from Neuroimaging and Implications for Deep Learning -- Real-Time Face Identification via Multi-Convolutional Neural Network and Boosted Hashing Forest -- CMS-RCNN: Contextual Multi-Scale Region-Based CNN for Unconstrained Face Detection -- Part II: Deep Learning for Fingerprint, Fingervein and Iris Recognition -- Latent Fingerprint Image Segmentation Using Deep Neural Networks -- Finger Vein Identification Using Convolutional Neural Network and Supervised Discrete Hashing -- Iris Segmentation Using Fully Convolutional Encoder-Decoder Networks -- Part III: Deep Learning for Soft Biometrics -- Two-Stream CNNs for Gesture-Based Verification and Identification: Learning User Style -- DeepGender2: A Generative Approach Toward Occlusion and Low Resolution Robust Facial Gender Classification via Progressively Trained Attention Shift Convolutional Neural Networks (PTAS-CNN) and Deep Convolutional Generative Adversarial Networks (DCGAN) -- Gender Classification from NIR Iris Images Using Deep Learning -- Deep Learning for Tattoo Recognition -- Part IV: Deep Learning for Biometric Security and Protection -- Learning Representations for Cryptographic Hash Based Face Template Protection -- Deep Triplet Embedding Representations for Liveness Detection.
520 $a This timely text/reference presents a broad overview of advanced deep learning architectures for learning effective feature representation for perceptual and biometrics-related tasks. The text offers a showcase of cutting-edge research on the use of convolutional neural networks (CNN) in face, iris, fingerprint, and vascular biometric systems, in addition to surveillance systems that use soft biometrics. Issues of biometrics security are also examined. Topics and features: Addresses the application of deep learning to enhance the performance of biometrics identification across a wide range of different biometrics modalities Revisits deep learning for face biometrics, offering insights from neuroimaging, and provides comparison with popular CNN-based architectures for face recognition Examines deep learning for state-of-the-art latent fingerprint and finger-vein recognition, as well as iris recognition Discusses deep learning for soft biometrics, including approaches for gesture-based identification, gender classification, and tattoo recognition Investigates deep learning for biometrics security, covering biometrics template protection methods, and liveness detection to protect against fake biometrics samples Presents contributions from a global selection of pre-eminent experts in the field representing academia, industry and government laboratories Providing both an accessible introduction to the practical applications of deep learning in biometrics, and a comprehensive coverage of the entire spectrum of biometric modalities, this authoritative volume will be of great interest to all researchers, practitioners and students involved in related areas of computer vision, pattern recognition and machine learning. Dr. Bir Bhanu is Bourns Presidential Chair, Distinguished Professor of Electrical and Computer Engineering and the Director of the Center for Research in Intelligent Systems at the University of California at Riverside, USA. Some of his other Springer publications include the titles Video Bioinformatics, Distributed Video Sensor Networks, and Human Recognition at a Distance in Video. Dr. Ajay Kumar is an Associate Professor in the Department of Computing at the Hong Kong Polytechnic University.
650 0 $a Biometric identification. $3 598553
650 0 $a Machine learning. $3 561253
650 1 4 $a Computer Science. $3 593922
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 593924
650 2 4 $a Biometrics. $3 677095
650 2 4 $a Mathematical Applications in Computer Science. $3 815331
700 1 $a Bhanu, Bir. $3 669791
700 1 $a Kumar, Ajay. $3 925704
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a Advances in computer vision and pattern recognition. $3 886855
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-61657-5
950 $a Computer Science (Springer-11645)