Mixture of Deep Regression Networks for Head Pose Estimation
Yangguang Huang, Lili Pan, Yali Zheng, Mei Hua Xie · 2018
Accurate and robust head pose estimation is a challenging computer vision task. In most existing methods, single-modal RGB or depth images are directly used for head pose estimation. The obvious drawbacks of these methods are two fold: (1) Traditional shallow models are not good at learning representative features. (2) They are single-modal approaches, resulting in sensitivity to noise. As such, in this work we propose a novel multi-modal regression model for head pose estimation, named mixture of deep regression networks (MoDRN). It only uses good examples for one modality to learn sub-network parameters. Thus, the sub-networks tend to be better trained and more robust to noise, making significant improved performance in their combination. Experiments on public datasets such as BIWI and BU-3DFE show the effectiveness of our approach.