Davide Maltoni – författare

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.

A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field. Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems. This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators.

The use of computers to recognize humans from physical and behavioral traits dates back to the digital computer evolution of the 1960s. But even after decades of research and hundreds of major deployments, the field of biometrics remains fresh and exciting as new technologies are developed andoldtechnologiesareimprovedandfieldedinnewapplications.Wor- wide over the past few years,there has been a marked increase in both g- ernment and private sector interest in large-scale biometric deployments for accelerating human–machine processes, efficiently delivering human services, fighting identity fraud and even combating terrorism. The p- pose of this book is to explore the current state of the art in biometrics- tems and it is the system aspect that we have wished to emphasize. By their nature, biometric technologies sit at the exact boundary of the human–machineinterface.Butlikealltechnologies,bythemselvestheycan provide no value until deployed in a system with support hardware, n- work connections, computers, policies and procedures, all tuned together to work withpeople to improve some real business process within a social structure.

The use of computers to recognize humans from physical and behavioral traits dates back to the digital computer evolution of the 1960s. But even after decades of research and hundreds of major deployments, the field of biometrics remains fresh and exciting as new technologies are developed andoldtechnologiesareimprovedandfieldedinnewapplications.Wor- wide over the past few years,there has been a marked increase in both g- ernment and private sector interest in large-scale biometric deployments for accelerating human–machine processes, efficiently delivering human services, fighting identity fraud and even combating terrorism. The p- pose of this book is to explore the current state of the art in biometrics- tems and it is the system aspect that we have wished to emphasize. By their nature, biometric technologies sit at the exact boundary of the human–machineinterface.Butlikealltechnologies,bythemselvestheycan provide no value until deployed in a system with support hardware, n- work connections, computers, policies and procedures, all tuned together to work withpeople to improve some real business process within a social structure.

The use of computers to recognize humans from physical and behavioral traits dates back to the digital computer evolution of the 1960s. But even after decades of research and hundreds of major deployments, the field of biometrics remains fresh and exciting as new technologies are developed andoldtechnologiesareimprovedandfieldedinnewapplications.Wor- wide over the past few years,there has been a marked increase in both g- ernment and private sector interest in large-scale biometric deployments for accelerating human–machine processes, efficiently delivering human services, fighting identity fraud and even combating terrorism. The p- pose of this book is to explore the current state of the art in biometrics- tems and it is the system aspect that we have wished to emphasize. By their nature, biometric technologies sit at the exact boundary of the human–machineinterface.Butlikealltechnologies,bythemselvestheycan provide no value until deployed in a system with support hardware, n- work connections, computers, policies and procedures, all tuned together to work withpeople to improve some real business process within a social structure.

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 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 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.

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.

Biometric authentication is increasingly gaining popularity in a large spectrum ofapplications,rangingfromgovernmentprograms(e. g. ,nationalIDcards,visas for international travel,and the ?ght against terrorism) to personal applications such as logical and physical access control. Although a number of e?ective - lutions are currently available, new approaches and techniques are necessary to overcomesomeofthelimitationsofcurrentsystemsandtoopenupnewfrontiers in biometric research and development. The 30 papers presented at Biometric Authentication Workshop 2004 (BioAW 2004) provided a snapshot of current research in biometrics, and identify some new trends. This volume is composed of?vesections:facerecognition,?ngerprintrecognition,templateprotectionand security, other biometrics, and fusion and multimodal biometrics. For classical biometrics like ?ngerprint and face recognition, most of the papers in Sect. 1 and 2 address robustness issues in order to make the biometric systems work in suboptimal conditions: examples include face detection and recognition - der uncontrolled lighting and pose variations, and ?ngerprint matching in the case of severe skin distortion.Benchmarking and interoperability of sensors and liveness detection are also topics of primary interest for ?ngerprint-based s- tems. Biometrics alone is not the solution for complex security problems. Some of the papers in Sect. 3 focus on designing secure systems; this requires dealing with safe template storage, checking data integrity, and implementing solutions in a privacy-preserving fashion. The match-on-tokens approach, provided that current accuracy and cost limitations can be satisfactorily solved by using new algorithms and hardware, is certainly a promising alternative. The use of new biometric indicators like eye movement, 3D ?nger shape, and soft traits (e. g.