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
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Cited by
Sorted by citations
Extended deep neural network for facial emotion recognition
Jain, D. K., Shamsolmoali, P., & Sehdev, P. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.01.008 · Cited by 417
Hybrid deep neural networks for face emotion recognition
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Deep learning for human affect recognition: Insights and new developments
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A Compact Deep Learning Model for Robust Facial Expression Recognition
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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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