Continuity Rotation Representation for Head Pose Estimation without Keypoints
Authors: 
Xiong Zhao, Sarina Sulaiman, Liang Chen, Min Dong, Yunfeng Duo, Hao SongAuthors Info & Claims
Pages 358 - 363
Abstract
In this paper, we present an improved end-to-end head pose estimation method in an unconstrained environment, which transforms the Head Pose Estimation(HPE) problem into a problem of directly predicting continuous 6D rotation matrix parameters belongs 3D Special Orthogonal Group(SO(3)). The method uses RepVGGplus-L2pse as the backbone, followed by one FC layer to output the results, model be trained on 300W-LP. The improved Root Mean Square Error of Geodesic Distance(RSME_GD) is used as the loss function to enhance the accuracy. The experiments on the two public face datasets AFLW-2000 and BIWI show that the results measured by Mean Absolute Error of Vectors (MAEV) are improved by 19.68% and 13.98% respectively compared with the original SOTA method.
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Index Terms
- Continuity Rotation Representation for Head Pose Estimation without Keypoints
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Published In
March 2023
824 pages
ISBN:9781450399029
DOI:10.1145/3594315
Copyright © 2023 ACM.
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Association for Computing Machinery
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Publication History
Published: 02 August 2023
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ICCAI 2023
ICCAI 2023: 2023 9th International Conference on Computing and Artificial Intelligence
March 17 - 20, 2023
Tianjin, China
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