Can Synthetic Data Handle Unconstrained Gaze EstimationReportar como inadecuado




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1 IRT b-com - Institut de Recherche Technologique b-com 2 CentraleSupélec 3 Orange Labs Lannion

Abstract : In this article, we aim at solving unconstrained gaze estimation problem using appearance-based approach. Unlike previous methods working in relatively constrained environment , we propose an approach that allows free head motion and significant user-sensor distances using RGB-D sensor. Our paper presents the following contributions : i A direct estimation by inferring gaze information from RGB eyes and depth face appearances ;ii A channel selection strategy during the learning to evaluate the involvement of each channel in the final prediction ; iii Adapting a 3D face morphable model by integrating a parametric gaze model to render an important synthetic RGB-D training set. We also collect real labeled samples using Kinect sensor that allows for evaluating the potential of synthetic learning in handling real configurations and establish an objective comparison with real learning. Results on several users demonstrate the great potential of our approach.

Keywords : Gaze estimation Eye tracking Random Forest Synthetic data 3D morphable model





Autor: Amine Kacete - Renaud Séguier - Michel Collobert - Jérôme Royan -

Fuente: https://hal.archives-ouvertes.fr/



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