Multi-Modal Emotion Recognition Combining Face Image and EEG Signal

Author:

Hu Ying1ORCID,Wang Feng2

Affiliation:

1. Department of Electrical Automation Engineering, ShanXi Polytechnic College, Taiyuan 030002, P. R. China

2. School of Information and Computer, Taiyuan University of Technology, Taiyuan 030000, P. R. China

Abstract

Face expression can be used to identify human emotions, but it is easy to misjudge when hidden artificially. In addition, the sentiment recognition of a single mode often results in low recognition rate due to the characteristics of the single mode itself. In order to solve the mentioned problems, the spatio-temporal neural network and the separable residual network proposed by fusion can realize the emotion recognition of EEG and face. The average recognition rates of EEG and face data sets are 78.14% and 70.89%, respectively, and the recognition rates of decision fusion on DEAP data sets are 84.53%. Experimental results show that compared with the single mode, the proposed two-mode emotion recognition architecture has better performance, and can well integrate the emotional information contained in human face visual signals and EEG signals.

Publisher

World Scientific Pub Co Pte Ltd

Subject

Electrical and Electronic Engineering,Hardware and Architecture,Electrical and Electronic Engineering,Hardware and Architecture

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