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Open AccessArticle

Ocular Biometrics Recognition by Analyzing Human Exploration during Video Observations

1
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, 29, Avenue J. F. Kennedy, 1855 Luxembourg, Luxembourg
2
Institute of Applied Sciences and Intelligent Systems, National Research Council of Italy, via Monteroni snc, 73100 Lecce, Italy
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(13), 4548; https://doi.org/10.3390/app10134548
Received: 5 May 2020 / Revised: 19 June 2020 / Accepted: 25 June 2020 / Published: 30 June 2020
Soft biometrics provide information about the individual but without the distinctiveness and permanence able to discriminate between any two individuals. Since the gaze represents one of the most investigated human traits, works evaluating the feasibility of considering it as a possible additional soft biometric trait have been recently appeared in the literature. Unfortunately, there is a lack of systematic studies on clinically approved stimuli to provide evidence of the correlation between exploratory paths and individual identities in “natural” scenarios (without calibration, imposed constraints, wearable tools). To overcome these drawbacks, this paper analyzes gaze patterns by using a computer vision based pipeline in order to prove the correlation between visual exploration and user identity. This correlation is robustly computed in a free exploration scenario, not biased by wearable devices nor constrained to a prior personalized calibration. Provided stimuli have been designed by clinical experts and then they allow better analysis of human exploration behaviors. In addition, the paper introduces a novel public dataset that provides, for the first time, images framing the faces of the involved subjects instead of only their gaze tracks. View Full-Text
Keywords: soft biometrics; human attention recognition; gaze estimation; public gaze dataset soft biometrics; human attention recognition; gaze estimation; public gaze dataset
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Cazzato, D.; Carcagnì, P.; Cimarelli, C.; Voos, H.; Distante, C.; Leo, M. Ocular Biometrics Recognition by Analyzing Human Exploration during Video Observations. Appl. Sci. 2020, 10, 4548.

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