The Efficient-CapsNet model for facial expression recognition
Author:
Funder
National Major Science and Technology Projects of China
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-022-04349-8.pdf
Reference29 articles.
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2. Chang T, Li H, Wen G, Hu Y, Ma J (2020) Correction to: Facial expression recognition sensing the complexity of testing samples. Appl Intell 50(11):4143–4144. https://doi.org/10.1007/s10489-020-01709-0
3. Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. https://doi.org/10.1109/CVPR.2017.195, pp 1251–1258
4. Fathallah A, Abdi L, Douik A (2017) Facial expression recognition via deep learning. In: 2017 IEEE/ACS 14th international conference on computer systems and applications (AICCSA). IEEE, pp 745–750, DOI https://doi.org/10.1109/AICCSA.2017.124, (to appear in print)
5. Georgescu MI, Ionescu RT, Popescu M (2019) Local learning with deep and handcrafted features for facial expression recognition. IEEE Access 7:64827–64836. https://doi.org/10.1109/ACCESS.2019.2917266
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. MVT-CEAM: a lightweight MobileViT with channel expansion and attention mechanism for facial expression recognition;Signal, Image and Video Processing;2024-06-17
2. CapsNet-based drift detection in data stream mining;Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems;2023-07-28
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