Papers › Hybrid coarse-fine classification for head pose estimation

Hybrid coarse-fine classification for head pose estimation

21 Jan 2019arXiv:1901.06778archive 2025-07-28

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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Code

haofanwang/accurate-head-pose officialmentioned in papermentioned on GitHubpytorch report

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Tasks

3D Reconstruction Classification Face Alignment Gaze Estimation General Classification Head Pose Estimation Pose Estimation Quantization regression

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Head Pose Estimation	AFLW	Hybrid Coarse-Fine	MAE	5.09	#3 of 6	Archive leaderboard	report
Head Pose Estimation	AFLW2000	Hybrid Coarse-Fine	MAE	5.395	#19 of 25	Archive leaderboard	report
Head Pose Estimation	BIWI	Hybrid Coarse-Fine	MAE (trained with BIWI data)	3.0174	#24 of 29	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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