Two Feature Fusion Network Based on Efficient Deep Residual Shrinkage Spatial-Temporal Graph Convolution for Emotion Recognition from Gaits

Emotion Recognition plays a key role in human social activities. Most current methods choose facial information for emotion recognition. But facial information is easily limited by distance in large scenes. Gait information can break this limitation, but the amount of publicly available data is small. This paper proposes two feature fusion network based on efficient deep residual shrinkage spatial-temporal graph convolution to achieve automatic emotion recognition from gaits in large scenes. Firstly chebyshev polynomial approximation graph convolution is combined with efficient deep residual shrinkage network to obtain efficient deep residual shrinkage spatial-temporal graph convolution network to extract deep features. Then sixteen temporal affective features extracted from gaits are used as input of efficient deep residual shrinkage network to extract new affective features. Finally the deep features is spliced with new affective features to divided emotion into happy,angry,sad and neutral. We performed a ten-flod cross-validation experiment on the E-gait real datasets. The highest recognition accuracy is 0.87, with an average of 0.85. Experimental results show that the proposed model is effective in dealing with emotion recognition promblem from gait in fewer sample situation.