About: Adapting to Movement Patterns for Face Recognition on Mobile Devices

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type	http://eprints.org/ontology/BookSectionEPrint http://eprints.org/ontology/EPrint Article Book Section
seeAlso	HTML Summary of #84439 Adapting to Movement Patterns for Face Recognition on Mobile Devices
sameAs	Adapting to Movement Patterns for Face Recognition on Mobile Devices
http://www.loc.gov...erms/relators/EDT	Roberto Vezzani Marco Bertini Giovanni Maria Farinella Tao Mei Hugo Jair Escalante Stan Sclaroff Alberto Del Bimbo Rita Cucchiara
http://eprints.org/ontology/hasAccepted	Adapting to Movement Patterns for Face Recognition on Mobile Devices (PDF)
http://eprints.org/ontology/hasDocument	Adapting to Movement Patterns for Face Recognition on Mobile Devices (PDF) Adapting to Movement Patterns for Face Recognition on Mobile Devices (Other) Adapting to Movement Patterns for Face Recognition on Mobile Devices (Other) Adapting to Movement Patterns for Face Recognition on Mobile Devices (Other) Adapting to Movement Patterns for Face Recognition on Mobile Devices (Other) Adapting to Movement Patterns for Face Recognition on Mobile Devices (Other)
dc:hasVersion	Adapting to Movement Patterns for Face Recognition on Mobile Devices (PDF)
Title	Adapting to Movement Patterns for Face Recognition on Mobile Devices
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2021-02-21
Creator	Farzin Deravi Matthew Boakes Richard Guest
status	peer reviewed published
Publisher	Springer International Publishing
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.
Is Part Of	https://kar.kent.ac.uk/id/repository
Subject	QA Mathematics (inc Computing science) TK7882.B56 Biometric identification
list of authors	https://kar.kent.ac.uk/id/eprint/84439#authors
list of editors	https://kar.kent.ac.uk/id/eprint/84439#editors
presented at	TC4 Workshop on Mobile and Wearable Biometrics Workshop
volume	12668
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #84439 Adapting to Movement Patterns for Face Recognition on Mobile Devices

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