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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

FDN: Feature Decoupling Network for Head Pose Estimation

February 1, 2023

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Authors

Hao Zhang

Institute of Cyber-Systems and Control, Zhejiang University


Mengmeng Wang

Institute of Cyber-Systems and Control, Zhejiang University


Yong Liu

Institute of Cyber-Systems and Control, Zhejiang University


Yi Yuan

NetEase Fuxi AI Lab


DOI:

10.1609/aaai.v34i07.6974


Abstract:

Head pose estimation from RGB images without depth information is a challenging task due to the loss of spatial information as well as large head pose variations in the wild. The performance of existing landmark-free methods remains unsatisfactory as the quality of estimated pose is inferior. In this paper, we propose a novel three-branch network architecture, termed as Feature Decoupling Network (FDN), a more powerful architecture for landmark-free head pose estimation from a single RGB image. In FDN, we first propose a feature decoupling (FD) module to explicitly learn the discriminative features for each pose angle by adaptively recalibrating its channel-wise responses. Besides, we introduce a cross-category center (CCC) loss to constrain the distribution of the latent variable subspaces and thus we can obtain more compact and distinct subspaces. Extensive experiments on both in-the-wild and controlled environment datasets demonstrate that the proposed method outperforms other state-of-the-art methods based on a single RGB image and behaves on par with approaches based on multimodal input resources.

Topics: AAAI

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HOW TO CITE:

Hao Zhang||Mengmeng Wang||Yong Liu||Yi Yuan FDN: Feature Decoupling Network for Head Pose Estimation Proceedings of the AAAI Conference on Artificial Intelligence (2020) 12789-12796.

Hao Zhang||Mengmeng Wang||Yong Liu||Yi Yuan FDN: Feature Decoupling Network for Head Pose Estimation AAAI 2020, 12789-12796.

Hao Zhang||Mengmeng Wang||Yong Liu||Yi Yuan (2020). FDN: Feature Decoupling Network for Head Pose Estimation. Proceedings of the AAAI Conference on Artificial Intelligence, 12789-12796.

Hao Zhang||Mengmeng Wang||Yong Liu||Yi Yuan. FDN: Feature Decoupling Network for Head Pose Estimation. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.12789-12796.

Hao Zhang||Mengmeng Wang||Yong Liu||Yi Yuan. 2020. FDN: Feature Decoupling Network for Head Pose Estimation. "Proceedings of the AAAI Conference on Artificial Intelligence". 12789-12796.

Hao Zhang||Mengmeng Wang||Yong Liu||Yi Yuan. (2020) "FDN: Feature Decoupling Network for Head Pose Estimation", Proceedings of the AAAI Conference on Artificial Intelligence, p.12789-12796

Hao Zhang||Mengmeng Wang||Yong Liu||Yi Yuan, "FDN: Feature Decoupling Network for Head Pose Estimation", AAAI, p.12789-12796, 2020.

Hao Zhang||Mengmeng Wang||Yong Liu||Yi Yuan. "FDN: Feature Decoupling Network for Head Pose Estimation". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.12789-12796.

Hao Zhang||Mengmeng Wang||Yong Liu||Yi Yuan. "FDN: Feature Decoupling Network for Head Pose Estimation". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 12789-12796.

Hao Zhang||Mengmeng Wang||Yong Liu||Yi Yuan. FDN: Feature Decoupling Network for Head Pose Estimation. AAAI[Internet]. 2020[cited 2023]; 12789-12796.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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