The DOI matches its Crossref record; 31 of the 32 references it cites were found in a registry or an index.
Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning
Abstract
This paper presents the techniques employed in our team's submissions to the 2015 Emotion Recognition in the Wild contest, for the sub-challenge of Static Facial Expression Recognition in the Wild. The objective of this sub-challenge is to classify the emotions expressed by the primary human subject in static images extracted from movies. We follow a transfer learning approach for deep Convolutional Neural Network (CNN) architectures. Starting from a network pre-trained on the generic ImageNet dataset, we perform supervised fine-tuning on the network in a two-stage process, first on datasets relevant to facial expressions, followed by the contest's dataset. Experimental results show that this cascading fine-tuning approach achieves better results, compared to a single stage fine-tuning with the combined datasets. Our best submission exhibited an overall accuracy of 48.5% in the validation set and 55.6% in the test set, which compares favorably to the respective 35.96% and 39.13% of the challenge baseline.
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“Since the number of labeled examples for FER remains relatively limited, large 11 models are likely to overfit the data [106], [107], [89], [108].”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [107]
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
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
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839
Robust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019
Return of the Devil in the Details: Delving Deep into Convolutional Nets
Chatfield, K., Simonyan, K., Vedaldi, A., & Zisserman, A. (2014) · Proceedings of the British Machine Vision Conference 2014 · doi:10.5244/c.28.6 · Cited by 3,335
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Research works printed but not linked: 1
- J. R. S. Widen and A. Brooks . Anger and disgust: Discrete or overlapping categories ? In Proceedings of the 2004 APS Annual Convention , 2004 . J. R. S. Widen and A. Brooks. Anger and disgust: Discrete or overlapping categories? In Proceedings of the 2004 APS Annual Convention, 2004.
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
“Instead of directly using the pretrained or fine-tuned models to extract features on the target dataset, a multistage fine-tuning strategy [67] can achieve better performance; after the first-stage fine-tuning using FER2013 on pretrained”
MethodReliable 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
“In EmotiW 2015 [8], more competitors have tried deep learning meth-ods: transfer learning was used to solve the problem of small database in [32], hierarchical committee of multi-column DCNNs in [20] gained the best result on SFEW database, LBP features combined with DCNNs structure were proposed…”
BackgroundDeep Learning vs. Traditional Computer Vision
O’Mahony, N., Campbell, S., Carvalho, A. L. D., Harapanahalli, S., Velasco-Hernandez, G., Krpálková, L., Riordan, D., & Walsh, J. L. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-17795-9_10 · Cited by 1,173
A Brief Review of Facial Emotion Recognition Based on Visual Information
Ko, B. C. (2018) · Sensors · doi:10.3390/s18020401 · Cited by 790
“Therefore, this sub-section briefly introduces some popular databases related to FER consisting of 2D and 3D video sequences and still images:”
BackgroundLi, S., & Deng, W. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2868382 · Cited by 688
““SFEW best [33]”, “SFEW second [77]” and “SFEW third [54]” indicate the best single model result of the winner, the runner-up and the second runner-up in EmotiW 2015, respectively.”
BackgroundMethodInfluential
Metadata
- Authors
- Ng, H.-W., Nguyen, V. D., Vonikakis, V., & Winkler, S.
- Affiliations
- University of Illinois at Urbana-Champaign (Singapore), Singapore, Singapore
- University of Illinois Urbana-Champaign (US)
- Venue
- Proceedings of the 2015 ACM on International Conference on Multimodal Interaction
- Publisher
- ACM
- Published
- 2015-11-09
- Pages
- 443-449
- OpenAlex
- W2253728219
- Cited by
- 657
- References
- 32
- Citations by year
- 2026: 13 · 2025: 29 · 2024: 37 · 2023: 70 · 2022: 69 · 2021: 106 · 2020: 106 · 2019: 106 · 2018: 63 · 2017: 44 · 2016: 13 · 2015: 1
- FWCI
- 53.84
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION (publishedVersion)
- Funders
- Agency for Science Technology and Research
- 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 › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- emotion recognition, facial expression recognition, still images, transfer learning, convolutional neural networks, fine-tuning, ImageNet pre-training, small datasets
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