Two-stream inter-class variation enhancement network for facial expression recognition-Reference-Cited by-同舟云学术

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Two-stream inter-class variation enhancement network for facial expression recognition

Published:2022-09-16 Issue: Volume: Page:
ISSN:0178-2789
Container-title:The Visual Computer
language:en
Short-container-title:Vis Comput

Author:

Jiang Qian,Zhang Ziyu,Da Feipeng,Gai Shaoyan

Funder

Basic Research of Frontier Leading Technology of Jiangsu Province of China

Natural Science Foundation of Jiangsu Province of China

Publisher

Springer Science and Business Media LLC

Subject

Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s00371-022-02655-3.pdf

Reference77 articles.

1. Ji, Y., Yang, Y., Shen, F., Shen, H.T., Li, X.: A survey of human action analysis in HRI applications. IEEE Trans. Circuits Syst. Video Technol. 30(7), 2114–2128 (2020). https://doi.org/10.1109/TCSVT.2019.2912988

2. Jeong, M., Ko, B.C.: Driver’s facial expression recognition in real-time for safe driving. Sensors (2018). https://doi.org/10.3390/s18124270

3. Zhang, F., Zhang, T., Mao, Q., Xu, C.: Joint pose and expression modeling for facial expression recognition. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3359–3368 (2018). https://doi.org/10.1109/CVPR.2018.00354

4. Kong, F.: Facial expression recognition method based on deep convolutional neural network combined with improved LBP features. Pers. Ubiquitous Comput. 23(3–4), 531–539 (2019). https://doi.org/10.1007/s00779-019-01238-9

5. Zhang, T., Zheng, W., Cui, Z., Zong, Y., Yan, J., Yan, K.: A deep neural network-driven feature learning method for multi-view facial expression recognition. IEEE Trans. Multimed. 18(12), 2528–2536 (2016). https://doi.org/10.1109/TMM.2016.2598092

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