Video Based Emotion Recognition Using CNN and BRNN
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Data augmentation artificially increases the number of available examples or training epochs by a factor typically between 10 and 30, and up to 300.
“This way, researchers artificially increase the number of available examples or training epochs by a factor typically between 10 and 30 [88], [113], [114], and up to 300 [115].”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [114]
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition
Jung, H., Lee, S., Yim, J., Park, S., & Kim, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.341 · Cited by 781 · see citing works
LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework
Wöllmer, M., Kaiser, M., Eyben, F., Schuller, B., & Rigoll, G. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2012.03.001 · Cited by 288 · see citing works
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Deep learning for human affect recognition: Insights and new developments
Rouast, P. V., Adam, M. T. P., & Chiong, R. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2890471 · Cited by 243 · see citing works
Thota, A., Sri, N. D., Dinesh, K., & Prasannatha, A. (2026) · 2026 9th International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei68323.2026.11542103
- Authors
- Cai, Y., Zheng, W., Zhang, T., Li, Q., Cui, Z., & Ye, J.
- Affiliations
- Southeast University (CN)
- Corresponding author
- Zheng, Wenming
- Venue
- Communications in Computer and Information Science
- Publisher
- Springer Singapore
- Published
- 2016
- Pages
- 679-691
- ISSN
- 1865-0929 (ISSN-L)
- 1865-0937
- OpenAlex
- W2536334481
- Cited by
- 20
- References
- 3
- Citations by year
- 2026: 1 · 2024: 3 · 2023: 3 · 2022: 2 · 2021: 2 · 2020: 3 · 2019: 2 · 2018: 4
- FWCI
- 2.31
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Communications in computer and information science (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- video-based emotion recognition, convolutional neural networks, bidirectional recurrent neural network, spatiotemporal feature extraction, facial texture, temporal dynamics of emotion, facial expression analysis
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