Robust Real-Time Extreme Head Pose Estimation
Sergey Tulyakov, Radu-Laurentiu Vieriu, Stanislau Semeniuta, Nicu Sebe · 2014
This paper proposes a new framework for head pose estimation under extreme pose variations. By augmenting the precision of a template matching based tracking module with the ability to recover offered by a frame-by-frame head pose estimator, we are able to address pose ranges for which face features are no longer visible, while maintaining state-of-the-art performance. Experimental results obtained on a newly acquired 3D extreme head pose dataset support the proposed method and open new perspectives in approaching real-life unconstrained scenarios.