The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns
Abstract
We present a novel method for classifying emotions from static facial images. Our approach leverages on the recent success of Convolutional Neural Networks (CNN) on face recognition problems. Unlike the settings often assumed there, far less labeled data is typically available for training emotion classification systems. Our method is therefore designed with the goal of simplifying the problem domain by removing confounding factors from the input images, with an emphasis on image illumination variations. This, in an effort to reduce the amount of data required to effectively train deep CNN models. To this end, we propose novel transformations of image intensities to 3D spaces, designed to be invariant to monotonic photometric transformations. These are applied to CASIA Webface images which are then used to train an ensemble of multiple architecture CNNs on multiple representations. Each model is then fine-tuned with limited emotion labeled training data to obtain final classification models. Our method was tested on the Emotion Recognition in the Wild Challenge (EmotiW 2015), Static Facial Expression Recognition sub-challenge (SFEW) and shown to provide a substantial, 15.36% improvement over baseline results (40% gain in performance).
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“Studies running experiments on this technique report accuracy improvements (e.g., from 79% to 89% in [87]; see also [74], [94]).”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [74]
Citations
Cites
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
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267
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
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
A comparative study of texture measures with classification based on featured distributions
Ojala, T., Pietikäinen, M., & Harwood, D. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00067-4 · Cited by 6,882
Research works printed but not linked: 5
- S. DaAZ Mello , N. Blanchard , R. Baker , J. Ocumpaugh , and K. Brawner . Affect-sensitive instructional strategies. Design Recommendations for Intelligent Tutoring Systems : Volume 2-Instructional Management, 2: 35 , 2014 . S. DaAZMello, N. Blanchard, R. Baker, J. Ocumpaugh, and K. Brawner. Affect-sensitive instructional strategies. Design Recommendations for Intelligent Tutoring Systems: Volume 2-Instructional Management, 2:35, 2014.
- 10.5555/795661.796155 · DOI
- 10.5555/646260.685274 · DOI
- D. Yi , Z. Lei , S. Liao , and S. Z. Li . Learning face representation from scratch. arXiv preprint arXiv:1411.7923 , 2014 . D. Yi, Z. Lei, S. Liao, and S. Z. Li. Learning face representation from scratch. arXiv preprint arXiv:1411.7923, 2014. · arXiv
- 10.5555/645308.648882 · DOI
Cited by
Sorted by citations
Deep Facial Expression Recognition: A Survey
Li, S., & Deng, W. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.2981446 · Cited by 2,175
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild
Li, S., Deng, W., & Du, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.277 · Cited by 1,470
Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition
Wang, K., Peng, X., Yang, J., Meng, D., & Qiao, Y. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2956143 · Cited by 970
Top-Down Neural Attention by Excitation Backprop
Zhang, J., Bargal, S. A., Lin, Z., Brandt, J., Shen, X., & Sclaroff, S. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1059-x · Cited by 815
Li, S., & Deng, W. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2868382 · Cited by 688
Metadata
- Authors
- Levi, G., & Hassner, T.
- Affiliations
- Open University of Israel (IL)
- University of Southern California (US)
- ORCID
- Hassner, Tal
- Venue
- Proceedings of the 2015 ACM on International Conference on Multimodal Interaction
- Publisher
- ACM
- Published
- 2015-11-09
- Pages
- 503-510
- OpenAlex
- W2251198138
- Cited by
- 336
- References
- 46
- Citations by year
- 2026: 4 · 2025: 15 · 2024: 17 · 2023: 17 · 2022: 24 · 2021: 48 · 2020: 49 · 2019: 62 · 2018: 46 · 2017: 44 · 2016: 9 · 2015: 1
- FWCI
- 44.50
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION (publishedVersion)
- Funders
- IARPA (2014-14071600010)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › 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
- emotion recognition, facial expression recognition, static facial images, convolutional neural networks, illumination invariance, photometric transformations, CASIA-WebFace
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