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020 _a9783319616575
_9978-3-319-61657-5
024 7 _a10.1007/978-3-319-61657-5
_2doi
050 4 _aQ334-342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
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082 0 4 _a006.3
_223
245 1 0 _aDeep Learning for Biometrics
_h[electronic resource] /
_cedited by Bir Bhanu, Ajay Kumar.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXXXI, 312 p. 117 illus., 96 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvances in Computer Vision and Pattern Recognition,
_x2191-6586
505 0 _aPart 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 _aThis 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 _aArtificial intelligence.
650 0 _aBiometrics (Biology).
650 0 _aComputer science—Mathematics.
650 0 _aComputer mathematics.
650 0 _aSignal processing.
650 0 _aImage processing.
650 0 _aSpeech processing systems.
650 1 4 _aArtificial Intelligence.
_0http://scigraph.springernature.com/things/product-market-codes/I21000
650 2 4 _aBiometrics.
_0http://scigraph.springernature.com/things/product-market-codes/I22040
650 2 4 _aMathematical Applications in Computer Science.
_0http://scigraph.springernature.com/things/product-market-codes/M13110
650 2 4 _aSignal, Image and Speech Processing.
_0http://scigraph.springernature.com/things/product-market-codes/T24051
700 1 _aBhanu, Bir.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aKumar, Ajay.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319616568
776 0 8 _iPrinted edition:
_z9783319616582
776 0 8 _iPrinted edition:
_z9783319871288
830 0 _aAdvances in Computer Vision and Pattern Recognition,
_x2191-6586
856 4 0 _uhttps://doi.org/10.1007/978-3-319-61657-5
912 _aZDB-2-SCS
999 _c446487
_d446487
942 _cEB
506 _aAvailable to subscribing member institutions only. Доступно лише організаціям членам підписки.
506 _fOnline access from local network of NaUOA.
506 _fOnline access with authorization at https://link.springer.com/
506 _fОнлайн-доступ з локальної мережі НаУОА.
506 _fОнлайн доступ з авторизацією на https://link.springer.com/