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Head pose estimation using facial-landmarks classification for children rehabilitation games
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DOI：10.1016/j.patrec.2021.11.002.png)
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Abstract
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During the last decade, there has been an increasing interest in developing Head-Pose Estimation (HPE) methods for different applications. Among these, there is the possibility to track a children's head pose during rehabilitation sessions and to use such information to control a virtual avatar, so to increase the engagement and the effectiveness of the exercises. This requires the ability to perform such tracking in real-time, with high precision, and considering wide set of tracking angles. HPE methods can be generally categorised either as appearance-based or model-based methods, while, in this paper, we propose a novel, simple but effective, hybrid method for estimating the Head-Pose. It starts by detecting the face followed by detecting robust feature points on it (facial landmarks). The second part consists of applying a classification mechanism to assign facial landmarks characterising a face to a predefined range of angles representing the face orientation. The obtained results allow using the proposed approach in realtime and showed the efficiency of this approach to get significant improvement compared to the state of the art. (c) 2021 Elsevier B.V. All rights reserved.
Keywords:
Head-pose estimation
CNN
SVM
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