A Bayesian hierarchy for robust gaze estimation in human–robot interaction

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Lanillos, P., Ferreira, J.F. ORCID: 0000-0002-2510-2412 and Dias, J., 2017. A Bayesian hierarchy for robust gaze estimation in human–robot interaction. International Journal of Approximate Reasoning, 87, pp. 1-22. ISSN 0888-613X

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Official URL: http://doi.org/10.1016/j.ijar.2017.04.007

Abstract

In this text, we present a probabilistic solution for robust gaze estimation in the context of human–robot interaction. Gaze estimation, in the sense of continuously assessing gaze direction of an interlocutor so as to determine his/her focus of visual attention, is important in several important computer vision applications, such as the development of non-intrusive gaze-tracking equipment for psychophysical experiments in neuroscience, specialised telecommunication devices, video surveillance, human–computer interfaces (HCI) and artificial cognitive systems for human–robot interaction (HRI), our application of interest. We have developed a robust solution based on a probabilistic approach that inherently deals with the uncertainty of sensor models, but also and in particular with uncertainty arising from distance, incomplete data and scene dynamics. This solution comprises a hierarchical formulation in the form of a mixture model that loosely follows how geometrical cues provided by facial features are believed to be used by the human perceptual system for gaze estimation. A quantitative analysis of the proposed framework's performance was undertaken through a thorough set of experimental sessions. Results show that the framework performs according to the difficult requirements of HRI applications, namely by exhibiting correctness, robustness and adaptiveness.

Item Type:

Journal article

Publication Title:

International Journal of Approximate Reasoning

Creators:

Lanillos, P., Ferreira, J.F. and Dias, J.

Publisher:

Elsevier

Date:

August 2017

Volume:

ISSN: