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Gaze Stability for Liveness Detection

Ali, Asad, Deravi, Farzin, Hoque, Sanaul (2018) Gaze Stability for Liveness Detection. Pattern Analysis and Applications, 21 (2). pp. 437-449. ISSN 1433-7541. (doi:10.1007/s10044-016-0587-2)

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Official URL
http://dx.doi.org/10.1007/s10044-016-0587-2

Abstract

Spoofing attacks on biometric systems are one of the major impediments to their use for secure unattended applications. This paper explores features for face liveness detection based on tracking the gaze of the user. In the proposed approach, a visual stimulus is placed on the display screen, at apparently random locations, which the user is required to follow while their gaze is measured. This visual stimulus appears in such a way that it repeatedly directs the gaze of the user to specific positions on the screen. Features extracted from sets of collinear and colocated points are used to estimate the liveness of the user. Data is collected from genuine users tracking the stimulus with natural head/eye movements and impostors holding a photograph, looking through a 2D mask or replaying the video of a genuine user. The choice of stimulus and features are based on the assumption that natural head/eye coordination for directing gaze results in a greater accuracy and thus can be used to effectively differentiate between genuine and spoofing attempts. Tests are performed to assess the effectiveness of the system with these features in isolation as well as in combination with each other using score fusion techniques. The results from the experiments indicate the effectiveness of the proposed gaze-based features in detecting such presentation attacks.

Item Type: Article
DOI/Identification number: 10.1007/s10044-016-0587-2
Uncontrolled keywords: biometrics, liveness, spoofing, fusion, presentation attacks, feature extraction
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1650 Facial Recognition systems
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Farzin Deravi
Date Deposited: 27 Oct 2016 14:54 UTC
Last Modified: 01 Aug 2019 10:40 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/58177 (The current URI for this page, for reference purposes)
Deravi, Farzin: https://orcid.org/0000-0003-0885-437X
Hoque, Sanaul: https://orcid.org/0000-0001-8627-3429
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