Group Emotion Recognition Based on Global and Local Features

Abstract

In order to improve the accuracy of group emotion recognition, a group emotion recognition model based on global scene feature and local face feature is constructed in this paper. When extracting global scene features, with the consideration of that the different size of the background objects may have different influences on the emotion recognition, the paper proposes a feature extraction algorithm for the global scene based on the fusion of multi-scale feature maps. With the consideration of the emotion propagation between different figures in the image, the paper proposes a LSTM based algorithm for fusion the face features among different figures. Experiments show that the global scene feature extraction algorithm proposed in this paper has higher accuracy than the global scene feature extraction algorithm based on standard network architecture. Besides, the facial emotion feature fusion algorithm based on LSTM has higher classification accuracy than the fusion algorithm based on average calculation and the algorithm based on voting. Besides, the group emotion recognition model proposed in this paper has an accuracy 24.38% higher than the benchmark method, 13.32% higher than the deep learning method and 14.57% higher than the deep learning method.

Keywords

• Deep two-way long-term and short-term memory network
• feature fusion
• global scene features
• group emotion recognition