2019/01/21 by Haofan Wang, Wang, Haofan, Zhenghua Chen +3
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face recognition and analysis #Hand Gesture Recognition Systems #Human Pose and Action Recognition #cs.CV
paper · pdf · doi:10.48550/arxiv.1901.06778
5 pages
openalex publication_date 2019/01/21 · arxiv created 2019/10/02 · arxiv updated 2019/10/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Head pose estimation, which computes the intrinsic Euler angles (yaw, pitch, roll) from the human, is crucial for gaze estimation, face alignment, and 3D reconstruction. Traditional approaches heavily relies on the accuracy of facial landmarks. It limits their performances, especially when the visibility of the face is not in good condition. In this paper, to do the estimation without facial landmarks, we combine the coarse and fine regression output together for a deep network. Utilizing more quantization units for the angles, a fine classifier is trained with the help of other auxiliary coarse units. Integrating regression is adopted to get the final prediction. The proposed approach is evaluated on three challenging benchmarks. It achieves the state-of-the-art on AFLW2000, BIWI and performs favorably on AFLW. The code has been released on Github.