Self-supervised facial expression recognition with fine-grained feature selection
Author:
Funder
Humanities and Social Science Fund of the Ministry of Education of the People’s Republic of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00371-024-03322-5.pdf
Reference46 articles.
1. Wang, K., Peng, X., Yang, J., Meng, D., Qiao, Y.: Region attention networks for pose and occlusion robust facial expression recognition. IEEE Trans. Image Process. 29, 4057–4069 (2020). https://doi.org/10.1109/tip.2019.2956143
2. Zhao, Z., Liu, Q., Wang, S.: Learning deep global multi-scale and local attention features for facial expression recognition in the wild. IEEE Trans. Image Process. 30, 6544–6556 (2021). https://doi.org/10.1109/tip.2021.3093397
3. Zheng, C., Mendieta, M., Chen, C.: Poster: a pyramid cross-fusion transformer network for facial expression recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3146–3155 (2023) https://doi.org/10.1109/iccvw60793.2023.00339
4. Mao, J., Xu, R., Yin, X., Chang, Y., Nie, B., Huang, A.: POSTER V2: a simpler and stronger facial expression recognition network. Preprint at arXiv:2301.12149. (2023) https://doi.org/10.48550/arXiv.2301.12149
5. Shi, J., Xiu, Y., Tang, G.: Research on occlusion block face recognition based on feature point location. Comput. Anim. Virtual Worlds 33(3–4), e2094 (2022). https://doi.org/10.1002/cav.2094
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