Mobile Biometric Device (MBD) Technology: Summary of Selected First Responder Experiences in Pilot Projects
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Counteracting Presentation Attacks in Face, Fingerprint and Iris Recognition
Counteracting Presentation Attacks in Face, Fingerprint and Iris Recognition Allan Pinto1, Helio Pedrini1, Michael Krumdick2, Benedict Becker2, Adam Czajka2,3,4, Kevin W. Bowyer2, and Anderson Rocha1 1Institute of Computing, University of Campinas, Brazil 2Computer Science and Engineering, University of Notre Dame, U.S.A. 3Research and Academic Computer Network (NASK), Poland 4Warsaw University of Technology, Poland April 1, 2017 Abstract This chapter explores data-driven approaches to presentation attack detection for three biometric modalities: face, iris and fingerprint. The primary aim of this chapter is to show how pre-trained deep neural networks can be used to build classifiers that can distinguish between authentic images of faces, irises and fingerprints and their static imitations. The most important, publicly available benchmarks representing various attack types were used in a unified presentation attack detection framework in both same-dataset and cross-dataset experiments. The pre-trained VGG neural networks, being the core of this solution, tuned independently for each modality and each dataset present almost perfect accuracy for all three biometric techniques. In turn, low classification accuracies achieved in cross-dataset evaluations show that models based on deep neural networks are sensitive not only to features specific to biometric imitations, but also to dataset-specific properties of samples. Thus, such models can provide a rapid solution in scenarios in which properties of imitations can be predicted but appropriate feature engineering is difficult. However, these models will perform worse if the properties of imitations being detected are unknown. This chapter includes also a current literature review summarizing up-to-date data-driven solutions to face, iris and finger liveness detection. -
Biometric Recognition: Challenges and Opportunities
This PDF is available from The National Academies Press at http://www.nap.edu/catalog.php?record_id=12720 Biometric Recognition: Challenges and Opportunities ISBN Joseph N. Pato and Lynette I. Millett, Editors; Whither Biometrics 978-0-309-14207-6 Committee; National Research Council 182 pages 6 x 9 PAPERBACK (2010) Visit the National Academies Press online and register for... Instant access to free PDF downloads of titles from the NATIONAL ACADEMY OF SCIENCES NATIONAL ACADEMY OF ENGINEERING INSTITUTE OF MEDICINE NATIONAL RESEARCH COUNCIL 10% off print titles Custom notification of new releases in your field of interest Special offers and discounts Distribution, posting, or copying of this PDF is strictly prohibited without written permission of the National Academies Press. Unless otherwise indicated, all materials in this PDF are copyrighted by the National Academy of Sciences. Request reprint permission for this book Copyright © National Academy of Sciences. All rights reserved. Biometric Recognition: Challenges and Opportunities Joseph N. Pato and Lynette I. Millett, Editors Whither Biometrics Committee Computer Science and Telecommunications Board Division on Engineering and Physical Sciences Copyright © National Academy of Sciences. All rights reserved. Biometric Recognition: Challenges and Opportunities THE NATIONAL ACADEMIES PRESS 500 Fifth Street, N.W. Washington, DC 20001 NOTICE: The project that is the subject of this report was approved by the Gov- erning Board of the National Research Council, whose members are drawn from the councils of the National Academy of Sciences, the National Academy of Engi- neering, and the Institute of Medicine. The members of the committee responsible for the report were chosen for their special competences and with regard for appropriate balance. -
Enhancing and Reinforcing Security and Usability of User Account Authentication Using Fingerprints As Username Credential
Enhancing and Reinforcing Security and Usability of User Account Authentication using Fingerprints as Username Credential by Mohammad Hassan Algarni A dissertation submitted to Florida Institute of Technology in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science Melbourne, Florida July 2018 We the undersigned committee hereby recommend that the attached dissertation be accepted as fulfilling in part the requirements for the degree of Doctor of Philosophy in Computer Science Enhancing and Reinforcing Security and Usability of User Account Authentication using Fingerprint as Username Credential by Mohammad Hassan Algarni Lucas Stephane, Ph.D. Assistant Professor, School of Computing, Human-Centered Design Innovation Program Advisor and Committee Chair Walter Bond, Ph.D. Associate Professor, School of Computing Marius Silaghi, Ph.D. Associate Professor, School of Computing Munevver Subasi, Ph.D. Associate Professor, Mathematical Sciences Philip J. Bernhard, Ph.D. Associate Professor, Director School of Computing Computer Sciences and Cybersecurity Abstract Title: Enhancing and Reinforcing Security and Usability of User Account Authentication using Fingerprints as Username Credential Author: Mohammad Hassan Algarni Committee Chair: Lucas Stephane, Ph.D. With the process of logging in, the users gain access to a computer system after identifying and authenticating themselves. The user credentials are required to login, and they are typically some form of a username and a matching password. The username for logging in an account is textual. This text can be either email address or some alphanumeric or numeric or characters chosen by the user. However, if the email account of a user is compromised, the attacker can click on Forgot Password link available on the user interface of the target account. -
From Password to Person the Evolution of Biometrics
From Password to Person The Evolution of Biometrics May 2020 Table of contents Executive Summary 01 Long-Range Iris Recognition 13 Knowledge and Recognition 02 Spoof and Liveness 14 Why Biometrics? 03 Detection Software A Superior Experience 04 Machine Learning and 14 Artificial Intelligence The Impact of the Smartphone 05 Expansion of Uses 14 Protecting Biometric Data 06 Internet of Things 14 Security, Convenience, and Thresholds 07 Travel 14 Usability Factors 07 Workplace 15 Fingerprint 08 Healthcare 15 Face 09 Ethical Considerations and Policy 16 Palm 10 Conclusion 17 Voice 10 Passive Biometrics and Behavioral Analytics 11 Recent Advances in Biometrics 13 Technology 13 Touchless Fingerprint Scanners 13 In-Display Fingerprint Readers 13 Fingerprint on Card 13 3D Facial Recognition 13 Executive Summary In recent years, identity verifiers have moved to address the vulnerabilities of knowledge-based identity data by employing biometric solutions. The verification of biometric data, liveness detection, and associated security processing are key areas of innovation. Physical biometrics such as fingerprint, face, or palm are being combined with technologies that recognize behavioral traits and associated devices to create seamless, intelligent, and more secure methods of authentication. This document: • Provides a comparative overview of different biometric modalities • Assesses security and usability issues • Reviews recent advances in technology and the expansion of uses • Discusses regulatory trends and ethical considerations From Password to Person: The Evolution of Biometrics was produced in association with the International Center for Biometric Research and The Center for Education and Research in Information Assurance and Security (CERIAS) at Purdue University. 1 Knowledge and Recognition How can you be confident that someone is who they say they are? Long ago, this wasn’t a problem.