Hybrid coarse-fine classification for head pose estimation

21 Jan 2019  ·  Haofan Wang, Zhenghua Chen, Yi Zhou ·

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.

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Head Pose Estimation AFLW Hybrid Coarse-Fine MAE 5.09 # 3
Head Pose Estimation AFLW2000 Hybrid Coarse-Fine MAE 5.395 # 19
Head Pose Estimation BIWI Hybrid Coarse-Fine MAE (trained with BIWI data) 3.0174 # 5

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