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With 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 decadesReviews the evolution of sensing technology: from bulky optical devices to in-display readers in smartphonesDedicates an entire new chapter to latent fingerprint recognition, which is nowadays feasible in “lights-out” modeIntroduces classical and learning-based techniques for local orientation extraction, enhancement, and minutiae detectionProvides an updated review of presentation-attack-detection techniques and their performance evaluationDiscusses 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 featuresReviews fingerprint synthesis, including recent Generative Adversarial NetworksThe 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.

With their proven 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 markedly enhanced second edition provides in-depth coverage of the recent advances and practices in fingerprint recognition. Readers will find comprehensive and authoritative coverage of all the major concepts, topics, and systems and security issues associated with fingerprint recognition systems. Written with the same formula that made the success of the first edition, this unique professional reference includes state-of-the-art techniques in fingerprint matching, and covers developments in sensor technology, performance evaluation, international standards, and system security. Features & Benefits:• Covers the latest research in fingerprint recognition algorithms and techniques • Reviews recent guidelines for scanner quality evaluation and certification, and provides examples of new fingerprint sensors • Provides introductory material on all components and modules of a fingerprint recognition system• Covers evaluations of fingerprint recognition algorithms and interoperability, including: FpVTE, MINEX, FVC2004 and FVC2006 • Integrates numerous supporting graphs, tables, charts, and performance data •Examines the design of secure fingerprint systems • Supplies an extensive annotated bibliography of citations and literature sources.• Contains helpful chapter overviews and summaries and consistent notation, for ease of use and accessibilityThe revised edition of this indispensible reference, written by leading international researchers, covers all critical aspects of fingerprint security systems 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 a graduate course on biometrics.Davide Maltoni is associate professor in the Department of Electronics, Informatics and Systems (DEIS) at the University of Bologna, where he also co-directs the Biometrics Systems Laboratory (BioLab).Dario Maio is full professor in the University of Bologna's DEIS and a co-director of the BioLab. He holds two patents on fingerprint liveness detection.Anil K. Jain is university-distinguished professor in the Department of Computer Science and Engineering at Michigan State University. He is a fellow of the IEEE, ACM and IAPR and holds six patents on algorithms for fingerprint recognition.Salil Prabhakar is the Chief Scientist of DigitalPersona Inc., a leading provider of fingerprint identity solutions for consumers, enterprises, and custom application developers.Key Topics• Fingerprint individuality• Fingerprint sensing • Biometric fusion• Synthetic fingerprint generation • Minutiae detection• Fingerprint system security • Performance evaluation• Feature extraction, matching, and indexing.

Overview Biometric recognition refers to the use of distinctive physiological and behavioral character- tics (e. g., fingerprints, face, hang geometry, iris, gait, signature), called biometric identifiers or simply biometrics, for automatically recognizing a person. Questions such as “Is this person authorized to enter the facility?”, “Is this individual entitled to access the privileged infor- tion?”, and “Did this person previously apply for a job?” are routinely asked in a variety of organizations in both public and private sectors. Because biometric identifiers cannot be easily misplaced, forged, or shared, they are considered more reliable for person recognition than traditional token- (e. g., keys) or knowledge- (e. g., password) based methods. Biometric recognition can provide better security, higher efficiency, and increased user convenience. It is for these reasons that biometric systems are being either increasingly deployed or evaluated in a large number of government (e. g., welfare disbursement, national ID card, issuing of driver's license) and civilian (e. g., computer network logon, automatic teller machine, cellular phone, Web access, smartcard) applications. A number of biometric technologies have been developed and several of them are being used in a variety of applications. Among these, fingerprints, face, iris, speech, and hand - ometry are the ones that are most commonly used. Each biometric has its strengths and we- nesses and the choice of a particular biometric typically depends on the requirements of an application.