2019 International Carnahan Conference on Security Technology (ICCST) 2019
DOI: 10.1109/ccst.2019.8888437
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Face Image Analysis in Mobile Biometric Accessibility Evaluations

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

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Cited by 5 publications
(2 citation statements)
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“…For accessibility evaluations, carried out for the development of this Thesis, were enrolled users affected by mobility and cognitive issues. A part of the motor and the cognitive concerns, some accessibility evaluations were carried out also recruiting older users [59], [60]. This is since several accessibility pathologies are age-related and, additionally, aging affects the user's dexterity and cognitive skills which influence their ability to provide high-quality biometric traits [35].…”
Section: Accessibility: Improvement Point In User Interaction Evaluationmentioning
confidence: 99%
“…For accessibility evaluations, carried out for the development of this Thesis, were enrolled users affected by mobility and cognitive issues. A part of the motor and the cognitive concerns, some accessibility evaluations were carried out also recruiting older users [59], [60]. This is since several accessibility pathologies are age-related and, additionally, aging affects the user's dexterity and cognitive skills which influence their ability to provide high-quality biometric traits [35].…”
Section: Accessibility: Improvement Point In User Interaction Evaluationmentioning
confidence: 99%
“…Regarding face, different studies assessed quality for face images considering different kinds of approaches and issues. Corsetti et al [10] investigated how accessibility influences the quality of captured image and therefore the authentication process, revealing how users with accessibility issues struggle providing good samples compared to control population. Chen et al [11] proposed a flexible ranking method to evaluate the quality of face images depending on the dataset and the authentication system in use, allowing to select the best performing images during the authentication process (when more than one is provided, for example in a video recording).…”
Section: Introductionmentioning
confidence: 99%