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 real-time and showed the efficiency of this approach to get significant improvement compared to the state of the art.

中文翻译：

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使用面部标志分类进行儿童康复游戏的头部姿势估计


在过去的十年中，人们对开发适用于不同应用的头部姿势估计 (HPE) 方法越来越感兴趣。其中，可以在康复训练期间跟踪儿童的头部姿势，并使用这些信息来控制虚拟化身，从而提高练习的参与度和有效性。这需要能够以高精度实时执行此类跟踪，并考虑广泛的跟踪角度。 HPE 方法通常可以分为基于外观的方法或基于模型的方法，而在本文中，我们提出了一种新颖、简单但有效的混合方法来估计头部姿势。它首先检测面部，然后检测其上的鲁棒特征点（面部标志）。第二部分包括应用分类机制将表征面部的面部标志分配给代表面部方向的预定义角度范围。获得的结果允许实时使用所提出的方法，并表明与现有技术相比，该方法的效率得到了显着的改进。