Work2016

How deep neural networks can improve emotion recognition on video data

Pooya Khorrami · Tom Le Paine · Kevin J. Brady · Charlie K. Dagli · Thomas S. Huang

107 citations0/0 files storedProceedings - International Conference on Image Processing

No readable copy stored yet — this record is metadata only.

Emotion and Mood Recognition Sentiment Analysis and Opinion Mining Face and Expression Recognition

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.

Citations in this library

Stored only when both papers are in the corpus — 107 citations exist upstream.

References

2010The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression 1977Evidence for a three-factor theory of emotions 2013Introducing the RECOLA multimodal corpus of remote collaborative and affective interactions 2013Combining modality specific deep neural networks for emotion recognition in video 2015Recurrent Neural Networks for Emotion Recognition in Video 2015Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?2013AU-aware Deep Networks for facial expression recognition 2014Prediction of asynchronous dimensional emotion ratings from audiovisual and physiological data

Cited by

2019Deep convolutional BiLSTM fusion network for facial expression recognition 2020Multi-Modal Recurrent Attention Networks for Facial Expression Recognition 2017Audio-visual emotion recognition using deep transfer learning and multiple temporal models 2018Enhanced speech emotion detection using deep neural networks 2019Deep Learning Approach for Emotion Recognition from Human Body Movements with Feedforward Deep Convolution Neural Networks

Imported from OpenAlex (CC0) — nothing here is generated.