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9783030836269: Handbook of Fingerprint Recognition

Sinopsis

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 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.

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Acerca del autor

Dr. Davide Maltoni and Dr. Dario Maio are full Professors in the Department of Computer Science and Engineering at the University of Bologna, Italy. Dr. Anil K. Jain is a University Distinguished Professor at the Department of Computer Science and Engineering at Michigan State University, USA. Dr. Jianjiang Feng is an Associate Professor in the Department of Automation at Tsinghua University, China.

The authors’ extensive list of publications on biometrics include the Springer titles Encyclopedia of Biometrics, Introduction to Biometrics, Handbook of Face Recognition, Handbook of Biometrics, Handbook of Multibiometrics, Human Identification Based on Gait, Biometric Systems, Palmprint Authentication.   

De la contraportada

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 knownas 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.

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9783030836238: Handbook of Fingerprint Recognition

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ISBN 10:  3030836231 ISBN 13:  9783030836238
Editorial: Springer-Verlag GmbH, 2022
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Paperback. Condición: new. Paperback. 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. A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field. This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9783030836269

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 visionand 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. 548 pp. Englisch. Nº de ref. del artículo: 9783030836269

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Maltoni, Davide|Maio, Dario|Jain, Anil K.|Feng, Jianjiang
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 . Nº de ref. del artículo: 888374276

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Maltoni, Davide (Author)/ Maio, Dario (Author)/ Jain, Anil K. (Author)/ Feng, Jianjiang (Author)
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 548 pp. Englisch. Nº de ref. del artículo: 9783030836269

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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 visionand 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. Nº de ref. del artículo: 9783030836269

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Paperback. Condición: new. Paperback. 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. A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field. This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9783030836269

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Maltoni, Davide; Maio, Dario; Jain, Anil K.; Feng, Jianjiang
Publicado por Springer, 2023
ISBN 10: 3030836266 ISBN 13: 9783030836269
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Maltoni, Davide; Maio, Dario; Jain, Anil K.; Feng, Jianjiang
Publicado por Springer, 2023
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