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  • © 2022

Handbook of Fingerprint Recognition

  • Using the successful formula of previous editions, the book includes recent state-of-the-art techniques

  • Covers all the major topics, concepts and methods for fingerprint security systems

  • Contains helpful chapter overviews and summaries and consistent notation, for ease of use and accessibility

Buying options

eBook USD 169.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-83624-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book USD 219.99
Price excludes VAT (USA)

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xxiv
  2. Introduction

    • Davide Maltoni, Dario Maio, Anil K. Jain, Jianjiang Feng
    Pages 1-62
  3. Fingerprint Sensing

    • Davide Maltoni, Dario Maio, Anil K. Jain, Jianjiang Feng
    Pages 63-114
  4. Fingerprint Analysis and Representation

    • Davide Maltoni, Dario Maio, Anil K. Jain, Jianjiang Feng
    Pages 115-216
  5. Fingerprint Matching

    • Davide Maltoni, Dario Maio, Anil K. Jain, Jianjiang Feng
    Pages 217-297
  6. Fingerprint Classification and Indexing

    • Davide Maltoni, Dario Maio, Anil K. Jain, Jianjiang Feng
    Pages 299-338
  7. Latent Fingerprint Recognition

    • Davide Maltoni, Dario Maio, Anil K. Jain, Jianjiang Feng
    Pages 339-383
  8. Fingerprint Synthesis

    • Davide Maltoni, Dario Maio, Anil K. Jain, Jianjiang Feng
    Pages 385-426
  9. Fingerprint Individuality

    • Davide Maltoni, Dario Maio, Anil K. Jain, Jianjiang Feng
    Pages 427-455
  10. Securing Fingerprint Systems

    • Davide Maltoni, Dario Maio, Anil K. Jain, Jianjiang Feng
    Pages 457-522

About this book

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.

Keywords

  • Performance
  • Classification
  • Fingerprint
  • Fingerprint Matching
  • Fingerprint Recognition
  • Fingerprint Sensing
  • Sensing
  • Policing

Authors and Affiliations

  • Department of Computer Science and Engineering, University of Bologna, Cesena, Italy

    Davide Maltoni, Dario Maio

  • Department of Computer Science and Engineering, Michigan State University, East Lansing, USA

    Anil K. Jain

  • Department of Automation, Tsinghua University, Beijing, China

    Jianjiang Feng

About the authors

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.   

Bibliographic Information

  • Book Title: Handbook of Fingerprint Recognition

  • Authors: Davide Maltoni, Dario Maio, Anil K. Jain, Jianjiang Feng

  • DOI: https://doi.org/10.1007/978-3-030-83624-5

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Switzerland AG 2022

  • Hardcover ISBN: 978-3-030-83623-8

  • eBook ISBN: 978-3-030-83624-5

  • Edition Number: 3

  • Number of Pages: XXIV, 522

  • Number of Illustrations: 187 b/w illustrations, 82 illustrations in colour

  • Additional Information: Originally published in the series: Springer Professional Computing, Springer Science+Business Media New York, The Editor(s) (if applicable) and The Author(s) (Legal Entity Name: Springer-Verlag London Ltd.)

  • Topics: Biometrics, Automated Pattern Recognition, Cybercrime, Machine Learning, Crime Control and Security

Buying options

eBook USD 169.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-83624-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book USD 219.99
Price excludes VAT (USA)