Emotion Recognition of Power Workers Based on Weighted Fusion

The emotion of power workers will directly affect their mental state and physical coordination. Emotional recognition of power workers can effectively reduce misjudgment and misoperation, thus reducing the occurrence of safety accidents. Therefore, this paper proposes an emotion recognition of power workers based on weighted fusion. The method first uses the voice activation detection (VAD) algorithm to extract speech key frames. Then, the information entropy is used to model that the generation of emotion is a continuous process, and the expression key frames are extracted. In addition, to fully use the complementarity between speech and expression modalities, the speech and expression key frame alignment techniques are utilized to calculate speech and expression weights. These weights are input into the feature fusion layer for weighted fusion, which effectively improves the recognition rate. Finally, the experimental results on the RML, eNTERFACE05 and BAUM-1s datasets show that the recognition rate of this method is higher than other benchmark methods. The experimental results also show that the method has high accuracy for emotion recognition of power workers.