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008			220704s2022 sz | s |||| 0|eng d
020			_a9783030836245
024	7		_a10.1007/978-3-030-83624-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
100	1		_aMaltoni, Davide _eautor _0(orcid)0000-0002-6329-6756 _1https://orcid.org/0000-0002-6329-6756 _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aHandbook of Fingerprint Recognition _cby Davide Maltoni, Dario Maio, Anil K Jain, Jianjiang Feng
250			_a3rd edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XXIV, 522 páginas) _b269 ilustraciones, 82 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
505	0		_aIntroduction -- Fingerprint sensing -- Fingerprint analysis and representation -- Fingerprint matching -- Fingerprint classification and indexing -- Latent fingerprint recognition -- Fingerprint synthesis -- Fingerprint individuality -- Securing fingerprint systems.
520			_aWith their distinctiveness and stability over time, fingerprints continue to be the most widely used anatomical characteristic in systems that automatically recognize a person's identity. This fully updated third edition provides in-depth coverage of the state-of-the-art in fingerprint recognition readers, feature extraction, and matching algorithms and applications. Deep learning (resurgence beginning around 2012) has been a game changer for artificial intelligence and, in particular, computer vision and biometrics. Performance improvements (both recognition accuracy and speed) for most biometric modalities can be attributed to the use of deep neural networks along with availability of large training sets and powerful hardware. Fingerprint recognition has also been approached by deep learning, resulting in effective and efficient methods for automated recognition and for learning robust fixed-length representations. However, the tiny ridge details in fingerprints known as minutiae are still competitive with the powerful representations learned by huge neural networks trained on big data. Features & Benefits: Reflects the progress made in automated techniques for fingerprint recognition over the past five decades Reviews the evolution of sensing technology: from bulky optical devices to in-display readers in smartphones Dedicates an entire new chapter to latent fingerprint recognition, which is nowadays feasible in "lights-out" mode Introduces classical and learning-based techniques for local orientation extraction, enhancement, and minutiae detection Provides an updated review of presentation-attack-detection techniques and their performance evaluation Discusses the evolution of minutiae matching from rich local descriptors to Minutiae Cylinder Code Presents the development of feature-based matching: from FingerCode to handcrafted textural features to deep features Reviews fingerprint synthesis, including recent Generative Adversarial Networks The revised edition of this must-read reference, written by leading international researchers, covers all critical aspects of fingerprint security system design and technology. It is an essential resource for all security and biometrics professionals, researchers, practitioners, developers, and systems administrators, and can serve as an easy-to-read reference for an undergraduate or graduate course on biometrics. Davide Maltoni is full professor in the Department of Computer Science (DISI) at the University of Bologna, where he also co-directs the Biometrics Systems Laboratory (BioLab). Dario Maio is full professor in the DISI and a co-director of the BioLab. Anil K. Jain is university distinguished professor in the Department of Computer Science and Engineering at Michigan State University. Jianjiang Feng is associate professor in the Department of Automation at Tsinghua University.
700	1		_aMaio, Dario _eautor _0(orcid)0000-0002-0094-0022 _1https://orcid.org/0000-0002-0094-0022 _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aJain, Anil K _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aFeng, Jianjiang _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783030836238
776	0	8	_iPrinted edition: _z9783030836252
776	0	8	_iPrinted edition: _z9783030836269
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-83624-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aSpringer_Computer_2022
999			_c394662 _d394662