What are you Looking at? A Preliminary Vision-Based Gaze Estimation Algorithm for Humanoid Robots in Educational Environments

This poster illustrates the implementation of a vision-based gaze estimation system related to the method presented in the paper cited below, titled What are you Looking at? A Preliminary Vision-Based Gaze Estimation Algorithm for Humanoid Robots in Educational Environments. The system predicts where a person is looking using images captured from a standard webcam, with the aim of exploring a low-cost, non-intrusive and accessible alternative to expensive traditional eye-tracking devices. The algorithm extracts head posture (yaw, pitch) and eye posture (iris-based descriptors) from facial landmarks and then applies a linear gaze estimation model to estimate the coordinates of the user's screen. As indicated in the study, combining head and eye posture descriptors can improve the precision and accuracy of gaze prediction compared to using head posture alone.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-10-01
DOI
https://doi.org/10.5281/zenodo.18243450
Primary Topic
Gaze Tracking and Assistive Technology
Type
article
Field-Weighted Citation Impact
0.00
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article

What are you Looking at? A Preliminary Vision-Based Gaze Estimation Algorithm for Humanoid Robots in Educational Environments

Selene Caro-Via, Carla Zou Yin Rodríguez-Rubio, Gemma Yebra-Berenguer, Alejandro González Alzate
Zenodo (CERN European Organization for Nuclear Research)
Gaze Tracking and Assistive Technology
article

What are you Looking at? A Preliminary Vision-Based Gaze Estimation Algorithm for Humanoid Robots in Educational Environments

Selene Caro-Via, Carla Zou Yin Rodríguez-Rubio, Gemma Yebra-Berenguer, Alejandro González Alzate
article en

Abstract

This poster illustrates the implementation of a vision-based gaze estimation system related to the method presented in the paper cited below, titled What are you Looking at? A Preliminary Vision-Based Gaze Estimation Algorithm for Humanoid Robots in Educational Environments. The system predicts where a person is looking using images captured from a standard webcam, with the aim of exploring a low-cost, non-intrusive and accessible alternative to expensive traditional eye-tracking devices. The algorithm extracts head posture (yaw, pitch) and eye posture (iris-based descriptors) from facial landmarks and then applies a linear gaze estimation model to estimate the coordinates of the user's screen. As indicated in the study, combining head and eye posture descriptors can improve the precision and accuracy of gaze prediction compared to using head posture alone.

Zenodo (CERN European Organization for Nuclear Research)
Universitat Ramon Llull (ES)
Quality Education
Openalex Percentile: Top 49%
Gaze Tracking and Assistive Technology
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