Cited by 107Emotion and Mood RecognitionSentiment Analysis and Opinion MiningFace and Expression Recognition

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

Khorrami, Pooya, Le Paine, Tom, Brady, Kevin, Dagli, Charlie, Huang, Thomas S.·2016·2016 IEEE International Conference on Image Processing (ICIP)

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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Crossref · 10.1109/icip.2016.7532431

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20 of 20 references verified0 citation errors · 0 not checkable

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Citations

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Research works printed but not linked: 3

  1. 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
  2. josh, 2010, The toronto face database, Department of Computer Science University of Toronto Toronto ON Canada Tech Rep
  3. 10.1037/0033-2909.115.2.268 · DOI · Paper page

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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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