Multi-layer temporal graphical model for head pose estimation in real-world videos

Head pose estimation has been receiving a lot of attention due to its wide range of possible applications. However, most approaches in the literature have focused on head pose estimation in controlled environments. Head pose estimation has recently begun to be applied to real-world environments. However, the focus has been on estimation from single images or video frames. Furthermore, most approaches frame the problem as classification into a set of coarse pose bins, rather than performing continuous pose estimation. The proposed multi-layer probabilistic temporal graphical model robustly estimates continuous head pose angle while leveraging the strengths of multiple features into account. Experiments performed on a large, real-world video database show that our approach not only significantly outperforms alternative head pose approaches, but also provides a pose probability assigned at each video frame, which permits robust temporal, probabilistic fusion of pose information over the entire video sequence.