Highly accurate and fully automatic 3D head pose estimation and eye gaze estimation using RGB-D sensors and 3D morphable models
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This work addresses the problem of automatic head pose estimation and its application in 3D gaze estimation using low quality RGB-D sensors without any subject cooperation or manual intervention. The previous works on 3D head pose estimation using RGB-D sensors require either an offline step for supervised learning or 3D head model construction, which may require manual intervention or subject cooperation for complete head model reconstruction. In this paper, we propose a 3D pose estimator based on low quality depth data, which is not limited by any of the aforementioned steps. Instead, the proposed technique relies on modeling the subject's face in 3D rather than the complete head, which, in turn, relaxes all of the constraints in the previous works. The proposed method is robust, highly accurate and fully automatic. Moreover, it does not need any offline step. Unlike some of the previous works, the method only uses depth data for pose estimation. The experimental results on the Biwi head pose database confirm the efficiency of our algorithm in handling large pose variations and partial occlusion. We also evaluated the performance of our algorithm on IDIAP database for 3D head pose and eye gaze estimation.
Ghiass , R S , Arandjelovic , O & Laurendeau , D 2018 , ' Highly accurate and fully automatic 3D head pose estimation and eye gaze estimation using RGB-D sensors and 3D morphable models ' , Sensors , vol. 18 , no. 12 , 4280 . https://doi.org/10.3390/s18124280
Copyright © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
DescriptionThe research presented in the paper was funded by grant F506-FSA of the Auto21 Networks of Centers of Excellence Program of Canada.
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