Maximum weight multi-modal information fusion algorithm of electroencephalographs and face images for emotion recognition

In view of the low accuracy of the traditional emotion recognition methods based on facial expressions, an emotion recognition method based on maximum weight multi-modal information fusion of electroencephalographs (EEGs) and facial expression information is proposed in this paper. First, the induced emotional EEG data is converted into the corresponding EEG topographic map data and sent to the convolutional network for training and outputting decision information. Second, the illumination compensation method is utilized to filter the noise of the face image data. Then, the face image data is trained in the multi-scale feature extraction network, and the decision information is output. Finally, aiming at the decision-level information fusion, a weighted fusion method is proposed in this paper for emotion recognition. Experimental tests show that the recognition accuracy of the multi-scale feature extraction network on the CK+ data set and Fer2013 data reached 94.4% and 72%, respectively. Simultaneously, the multimodal information fusion method achieves 92.6% accuracy in emotion recognition.

Automated summary

This paper proposes an emotion recognition method based on the fusion of EEG and facial expression information, utilizing convolutional networks and multi-scale feature extraction networks for training and decision output, and applying a weighted fusion method for emotion recognition, with high accuracy achieved.