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

Pablo Lanillos, João Filipe Ferreira, Jorge Dias

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageBritish English
Pages (from-to)1-22
Number of pages22
JournalInternational Journal of Approximate Reasoning
StatePublished - 1 Aug 2017


  • Bayesian estimation
  • Gaze estimation
  • Head pose estimation
  • HRI
  • Robustness to distance
  • Robustness to missing data


Dive into the research topics of 'A Bayesian hierarchy for robust gaze estimation in human–robot interaction'. Together they form a unique fingerprint.

Cite this