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Noise Analysis in Audio-Visual Emotion Recognition

This paper describes the use of a decision-based fusion framework to infer emotion from audiovisual feeds, and investigates the effect of noise on the fusion system. Facial expression features are constructed from linear binary patterns, and are processed independently of the prosodic features. A linear support vector machine is used for the fusion of the two channels. The results show that the recognition accuracy of the bimodal system improves on the individual channels; moreover, the system maintains a reasonably good performance in the presence of noise.