The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
How deep neural networks can improve emotion recognition on video data
Abstract
We consider the task of dimensional emotion recognition on video data using deep learning. While several previous methods have shown the benefits of training temporal neural network models such as recurrent neural networks (RNNs) on hand-crafted features, few works have considered combining convolutional neural networks (CNNs) with RNNs. In this work, we present a system that performs emotion recognition on video data using both CNNs and RNNs, and we also analyze how much each neural network component contributes to the system's overall performance. We present our findings on videos from the Audio/Visual+Emotion Challenge (AV+EC2015). In our experiments, we analyze the effects of several hyperparameters on overall performance while also achieving superior performance to the baseline and other competing methods.
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“Global temporal modeling is found to lead to improved accuracies when compared with simpler methods such as pooling of spatial features (e.g., [152], [90], [108]).”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [90]
In the citing paper
“We see both CNN-RNN (e.g., [18], [90]) and CNN-LSTM (e.g., [108], [105], [64]) architectures, with CNN-LSTM being the more frequent choice among the reviewed studies.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [90]
In the citing paper
“However, most of these approaches train individual components separately (e.g., [106], [90]), which may lead to suboptimal performance considering the whole system.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [90]
Citations
Cites
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Ringeval, F., Sonderegger, A., Sauer, J., & Lalanne, D. (2013) · 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) · doi:10.1109/fg.2013.6553805 · Cited by 771
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Research works printed but not linked: 3
- valstar, 2010, Induced disgust, happiness and surprise: an addition to the mmi facial expression database, Proceedings of the 3rd International Workshop of Emotion Corpora for Research on Emotion and Affect
- josh, 2010, The toronto face database, Department of Computer Science University of Toronto Toronto ON Canada Tech Rep
- 10.1037/0033-2909.115.2.268 · DOI · Paper page
Cited by
Sorted by citations
Items 101–106 of 106
Sensory Maze: A camera-based remote expression therapy interface
Sengupta, K. (2023) · International Conference on Information Control Systems & Technologies · doi:10.1109/icst59744.2023.10460799 · Cited by 1
Decoding Student Sentiments: Academic Emotion Prediction with CNN-ResNet
Rathi, S., Khedkar, V. N., Ghule, G., Diwan, N., & Atre, K. (2024) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec61587.2024.10722135
Game Analysis Using Multimodal Emotion Recognition
Kulkarni, R. G., Rajeevalochana, S., Vishwanatha, A., Sharanya, S., & Narayan, S. (2025) · International Conference on Computer and Communication Systems (ICCCS) · doi:10.1109/icccs65393.2025.11069702
Lei, Y., Wu, Y., Yao, R., & Fan, H. (2025) · International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste64638.2025.11092263
Abdallah, H. H., & Kurmasha, H. T. R. (2025) · International Conference on Business Analytics for Technology and Security (ICBATS) · doi:10.1109/icbats66542.2025.11258476
Videodaki Yüz İfadeleri Üzerinden Duygu Analizi
EKMEKÇİ, K., & Özbay, S. (2021) · DergiPark (Istanbul University)
Metadata
- Authors
- Khorrami, P., Le Paine, T., Brady, K., Dagli, C., & Huang, T. S.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- MIT Lincoln Laboratory (US)
- Venue
- 2016 IEEE International Conference on Image Processing (ICIP)
- Publisher
- IEEE
- Published
- 2016-09
- Pages
- 619-623
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2281407413
- Cited by
- 107
- References
- 20
- Citations by year
- 2025: 4 · 2024: 7 · 2023: 7 · 2022: 8 · 2021: 25 · 2020: 16 · 2019: 19 · 2018: 12 · 2017: 8 · 2016: 1
- FWCI
- 15.42
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - International Conference on Image Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- video-based emotion recognition, convolutional neural networks, recurrent neural networks, hybrid CNN-RNN model, hyperparameter analysis, temporal modeling
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