Disentangling Factors of Variation for Facial Expression Recognition
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Deep learning is used for learning spatial feature representations in human affect recognition.
“Learning spatial feature representations. For images [61], short-term 6 image sequences [62] and audio segments [54], DNNs and especially CNNs are used to learn spatial feature representations.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [61]
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Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589 · see citing works
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891 · see citing works
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841 · see citing works
Self-citationWhat is the best multi-stage architecture for object recognition?
Jarrett, K., Kavukcuoglu, K., Ranzato, M. A., & LeCun, Y. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459469 · Cited by 2,153 · see citing works
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Mao, Q., Dong, M., Huang, Z., & Zhan, Y. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2014.2360798 · Cited by 568 · see citing works
Corneanu, C. A., Simon, M. O., Cohn, J. F., & Guerrero, S. E. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2515606 · Cited by 527 · see citing works
FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition
Ding, H., Zhou, S. K., & Chellappa, R. (2017) · 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) · doi:10.1109/fg.2017.23 · Cited by 416 · see citing works
Learning deep physiological models of affect
Martinez, H. P., Bengio, Y., & Yannakakis, G. N. (2013) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2013.2247823 · Cited by 287 · see citing works
Self-citationDo Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?
Khorrami, P., Paine, T. L., & Huang, T. S. (2015) · 2015 IEEE International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2015.12 · Cited by 285 · see citing works
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
Multimodal 2D+3D Facial Expression Recognition With Deep Fusion Convolutional Neural Network
Li, H., Sun, J., Xu, Z., & Chen, L. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2713408 · Cited by 211 · see citing works
From neural PCA to deep unsupervised learning
Valpola, H. (2015) · Advances in Independent Component Analysis and Learning Machines · doi:10.1016/b978-0-12-802806-3.00008-7 · Cited by 181 · see citing works
Facial Expression Recognition with Identity and Emotion Joint Learning
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Expression Recognition Method Based on a Lightweight Convolutional Neural Network
Zhao, G., Yang, H., & Yu, M. (2020) · IEEE Access · doi:10.1109/access.2020.2964752 · Cited by 53 · see citing works
Emotion recognition from thermal infrared images using deep Boltzmann machine
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- Authors
- Rifai, S., Bengio, Y., Courville, A., Vincent, P., & Mirza, M.
- Affiliations
- Université de Montréal (CA)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2012
- Pages
- 808-822
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W171902450
- Cited by
- 215
- References
- 5
- Citations by year
- 2025: 9 · 2024: 8 · 2023: 17 · 2022: 22 · 2021: 18 · 2020: 18 · 2019: 22 · 2018: 15 · 2017: 28 · 2016: 21 · 2015: 16 · 2014: 10 · 2013: 10 · 2012: 1
- FWCI
- 22.89
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- facial expression recognition, emotion recognition, variation factors, semi-supervised learning, translation invariance, pose invariance, identity invariance, feature hierarchy
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