Continuity rotation representation for head pose estimation without keypoints
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 u...
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my.utm.1078652024-10-08T06:41:47Z http://eprints.utm.my/107865/ Continuity rotation representation for head pose estimation without keypoints Zhao, Xiong Sulaiman, Sarina Chen, Liang Dong, Min Duo, Yunfeng Song, Hao QA75 Electronic computers. Computer science 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 RepVGGplusL2pse 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. 2023 Conference or Workshop Item PeerReviewed Zhao, Xiong and Sulaiman, Sarina and Chen, Liang and Dong, Min and Duo, Yunfeng and Song, Hao (2023) Continuity rotation representation for head pose estimation without keypoints. In: 9th International Conference on Computing and Artificial Intelligence, ICCAI 2023, 17 March 2023-20 March 2023, Tianjin, China. http://dx.doi.org/10.1145/3594315.3594341 |
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QA75 Electronic computers. Computer science Zhao, Xiong Sulaiman, Sarina Chen, Liang Dong, Min Duo, Yunfeng Song, Hao Continuity rotation representation for head pose estimation without keypoints |
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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 RepVGGplusL2pse 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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Conference or Workshop Item |
author |
Zhao, Xiong Sulaiman, Sarina Chen, Liang Dong, Min Duo, Yunfeng Song, Hao |
author_facet |
Zhao, Xiong Sulaiman, Sarina Chen, Liang Dong, Min Duo, Yunfeng Song, Hao |
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Zhao, Xiong |
title |
Continuity rotation representation for head pose estimation without keypoints |
title_short |
Continuity rotation representation for head pose estimation without keypoints |
title_full |
Continuity rotation representation for head pose estimation without keypoints |
title_fullStr |
Continuity rotation representation for head pose estimation without keypoints |
title_full_unstemmed |
Continuity rotation representation for head pose estimation without keypoints |
title_sort |
continuity rotation representation for head pose estimation without keypoints |
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2023 |
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http://eprints.utm.my/107865/ http://dx.doi.org/10.1145/3594315.3594341 |
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1814043543563403264 |
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13.209306 |