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020			_a9783030325831
024	7		_a10.1007/978-3-030-32583-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK7882 .B56 _b2020 EB
245	0	0	_aDeep Biometrics _cedited by Richard Jiang, Chang-Tsun Li, Danny Crookes, Weizhi Meng, Christophe Rosenberger
250			_aPrimera edición 2020
264		1	_aCham _bSpringer _c2020
300			_a1 recurso en línea (VIII, 320 páginas) _b 118 ilustraciones, 99 ilustraciones a color.
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aUnsupervised and Semi-Supervised Learning _x2522-848X
490	0		_aEngineering (Springer-11647)
505	0		_aIntroduction -- Part I - New Methods in Biometrics -- Deep Biometrics: A Robust Approach to Biometrics in Big Data Issues -- Deep Fusion of Multimodal Biometrics -- Deep Fuzzy Logic for Precise Biometric Systems -- Hierarchical Biometric Verification with Deep Sparse Features -- GAN-based Deep Biometric Verification -- Part II - New Advances in Deep Biometrics -- Deep Paleographic Handwriting Analysis for Author Identification -- Deep Palmprints versus Fingerprints: Rivals or Friends? -- A Survey on Deep Soft Biometrics for Forensic Analysis -- Robust Biometric Verification with Low Quality Data -- Deep Solution for Biometric Big Data -- Deep Privacy in Biometric -- Part III - New Biometric Applications using Deep Learning -- Biometric Key Generation via Deep Learning for Mobile Banking -- Securing Electronic Medical Records Using Deep Biometric Authentication -- Deep Body Biometrics from MRI Images for Medicine Advice -- Deep Social Identity in Social Network -- Deep Cognition in Robotic Biometrics -- Conclusion.
520	3		_aThis book highlights new advances in biometrics using deep learning toward deeper and wider background, deeming it "Deep Biometrics". The book aims to highlight recent developments in biometrics using semi-supervised and unsupervised methods such as Deep Neural Networks, Deep Stacked Autoencoder, Convolutional Neural Networks, Generative Adversary Networks, and so on. The contributors demonstrate the power of deep learning techniques in the emerging new areas such as privacy and security issues, cancellable biometrics, soft biometrics, smart cities, big biometric data, biometric banking, medical biometrics, healthcare biometrics, and biometric genetics, etc. The goal of this volume is to summarize the recent advances in using Deep Learning in the area of biometric security and privacy toward deeper and wider applications. Highlights the impact of deep learning over the field of biometrics in a wide area; Exploits the deeper and wider background of biometrics, such as privacy versus security, biometric big data, biometric genetics, and biometric diagnosis, etc.; Introduces new biometric applications such as biometric banking, internet of things, cloud computing, and medical biometrics.
988			_aPrimersemestre_2020_Engineering
650		7	_2embne _aIdentificación biométrica _9687509
700	1		_aJiang, Richard _eeditor
700	1		_aLi, Chang-Tsun _eeditor _0(orcid)0000-0003-4735-6138
700	1		_aCrookes, Danny _eeditor
700	1		_aMeng, Weizhi _eeditor
700	1		_aRosenberger, Christophe _eeditor
710	2		_aSpringerLink (Online service) _9106996
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783030325824
776	0	8	_iPrinted edition: _z9783030325848
776	0	8	_iPrinted edition: _z9783030325855
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-32583-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b04/2020 _dz _eu _zSI