Research on Depression Recognition Based on University Students’ Facial Expressions and Actions with the Assistance of Artificial Intelligence

Abstract

As artificial intelligence (AI) technology advances, its application in the field of psychology has witnessed significant advancements. In this paper, with the assistance of AI, 80 university students with depression and 80 university students with normal psychology were selected as the subjects. The facial expression feature data were extracted through OpenFace, and the action feature data were extracted based on a Kinect camera. Then, the convolutional neural network-long short-term memory (CNN-LSTM) and temporal convolutional neural network (TCN) approaches were designed for recognition. Finally, a weighted fusion recognition method was proposed. The results showed that compared with the support vector machine, back-propagation neural network, and other approaches, the CNN-LSTM and TCN methods showed better performance in the recognition of single feature data, and the accuracy reached 0.781 and 0.769, respectively. After weighted fusion, the accuracy reached the highest at 0.875. The results verify that the methods designed in this paper are effective in identifying depressive emotions through facial expressions and actions among university students and have the potential for practical application.