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001447986 001__ 1447986
001447986 003__ OCoLC
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001447986 019__ $$a1334887118
001447986 020__ $$a9783030836245$$q(electronic bk.)
001447986 020__ $$a303083624X$$q(electronic bk.)
001447986 020__ $$z9783030836238
001447986 020__ $$z3030836231
001447986 0247_ $$a10.1007/978-3-030-83624-5$$2doi
001447986 035__ $$aSP(OCoLC)1334660952
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001447986 049__ $$aISEA
001447986 050_4 $$aHV6074
001447986 08204 $$a363.25/8$$223/eng/20220713
001447986 24500 $$aHandbook of fingerprint recognition.
001447986 24630 $$aFingerprint recognition
001447986 250__ $$aThird edition /$$bDavide Maltoni, Dario Maio, Anil K. Jain, Jianjiang Feng.
001447986 264_1 $$aCham :$$bSpringer,$$c[2022]
001447986 264_4 $$c©2022
001447986 300__ $$a1 online resource :$$billustrations (some color)
001447986 336__ $$atext$$btxt$$2rdacontent
001447986 337__ $$acomputer$$bc$$2rdamedia
001447986 338__ $$aonline resource$$bcr$$2rdacarrier
001447986 500__ $$aPrevious edition: 2009.
001447986 504__ $$aIncludes bibliographical references.
001447986 5050_ $$aIntroduction -- Fingerprint sensing -- Fingerprint analysis and representation -- Fingerprint matching -- Fingerprint classification and indexing -- Latent fingerprint recognition -- Fingerprint synthesis -- Fingerprint individuality -- Securing fingerprint systems.
001447986 506__ $$aAccess limited to authorized users.
001447986 520__ $$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.
001447986 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed July 13, 2022).
001447986 650_0 $$aFingerprints$$xIdentification.
001447986 650_0 $$aFingerprints$$xClassification.
001447986 650_6 $$aEmpreintes digitales$$0(CaQQLa)201-0047092$$xIdentification.$$0(CaQQLa)201-0378359
001447986 650_6 $$aEmpreintes digitales$$0(CaQQLa)201-0047092$$xClassification.$$0(CaQQLa)201-0378392
001447986 655_0 $$aElectronic books.
001447986 655_7 $$aClassification.$$2fast$$0(OCoLC)fst01697073
001447986 7001_ $$aMaltoni, Davide,$$eeditor.$$1https://isni.org/isni/0000000116073894
001447986 7001_ $$aMaio, Dario,$$eeditor.
001447986 7001_ $$aJain, Anil K.,$$d1948-$$eeditor.
001447986 7001_ $$aFeng, Jianjiang,$$eeditor.
001447986 77608 $$iPrint version: $$z3030836231$$z9783030836238$$w(OCoLC)1259585569
001447986 852__ $$bebk
001447986 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-83624-5$$zOnline Access$$91397441.1
001447986 909CO $$ooai:library.usi.edu:1447986$$pGLOBAL_SET
001447986 980__ $$aBIB
001447986 980__ $$aEBOOK
001447986 982__ $$aEbook
001447986 983__ $$aOnline
001447986 994__ $$a92$$bISE