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Adapting to Movement Patterns for Face Recognition on Mobile Devices

Boakes, Matthew and Guest, Richard and Deravi, Farzin (2021) Adapting to Movement Patterns for Face Recognition on Mobile Devices. In: Del Bimbo, Alberto and Cucchiara, Rita and Sclaroff, Stan and Farinella, Giovanni Maria and Mei, Tao and Bertini, Marco and Escalante, Hugo Jair and Vezzani, Roberto, eds. Pattern Recognition. ICPR International Workshops and Challenges. Lecture Notes in Computer Science, 12668 . Springer International Publishing, Cham, pp. 209-228. ISBN 978-3-030-68792-2. E-ISBN 978-3-030-68793-9. (doi:10.1007/978-3-030-68793-9_15) (KAR id:84439)

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http://dx.doi.org/10.1007/978-3-030-68793-9_15

Abstract

Facial recognition is becoming an increasingly popular way to authenticate users, helped by the increased use of biometric technology within mobile devices, such as smartphones and tablets. Biometric systems use thresholds to identify whether a user is genuine or an impostor. Traditional biometric systems are static (such as eGates at airports), which allow the operators and developers to create an environment most suited for the successful operation of the biometric technology by using a fixed threshold value to determine the authenticity of the user. However, with a mobile device and scenario, the operational conditions are beyond the control of the developers and operators.

In this paper, we propose a novel approach to mobile biometric authentication within a mobile scenario, by offering an adaptive threshold to authenticate users based on the environment, situations and conditions in which they are operating the device. Utilising smartphone sensors, we demonstrate the creation of a successful scenario classification. Using this, we propose our idea of an extendable framework to allow multiple scenario thresholds. Furthermore, we test the concept with data collected from a smartphone device. Results show that using an adaptive scenario threshold approach can improve the biometric performance, and hence could allow manufacturers to produce algorithms that perform consistently in multiple scenarios without compromising security, allowing an increase in public trust towards the use of the technology.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-030-68793-9_15
Additional information: The final authenticated version is available online at https://doi.org/10.1007/978-3-030-68793-9_15.
Uncontrolled keywords: Mobile, Face, Adaptive, Threshold, Motion, Scenario, Classification
Subjects: Q Science > QA Mathematics (inc Computing science)
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK7800 Electronics > TK7880 Applications of electronics > TK7882.B56 Biometric identification
Divisions: University wide Teaching/Research Centres > Kent Interdisciplinary Research Centre in Cyber Security
Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Matthew Boakes
Date Deposited: 25 Nov 2020 12:14 UTC
Last Modified: 23 Mar 2021 22:06 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/84439 (The current URI for this page, for reference purposes)
Boakes, Matthew: https://orcid.org/0000-0002-9377-6240
Guest, Richard: https://orcid.org/0000-0001-7535-7336
Deravi, Farzin: https://orcid.org/0000-0003-0885-437X
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