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A Combined Reinforcement Regression Model Based on Weighted Feedback for Multimodal Emotion Recognition

Automatic emotion recognition has become more and more important in human-computer interaction. Although several approaches have been presented in emotion recognition, some problems remain open. This paper presents a model structure of automatic emotion recognition based on multimodal (audio and visual) in continuous dimensional emotional space. A combined reinforcement regression model (CRRM) structure is built based on gradient boosting regression (GBR) and support vector regression (SVR) to process visual features and audio features, respectively. And then, two weighted fusion methods are added to deal with outliers in the predicted result of audio and visual. The data set is the LIRIS-ACCEDE database. Finally, the fusion results increased by 15.88% and 6.39% compared the predicted result of audio and visual in arousal space, and increased by 4.03% and 5.94% in valence space, respectively. There are 6.23% and 6.04% increases than linear regression fusion method. The results show that this recognition structure is superior and the fusion results are reliable.