Expression Identification and Emotional Classification of Students in Job Interviews Based on Image Processing

Abstract

The performance of college students in job interviews can be significantly promoted, if they are guided properly to identify and regulate negative emotions. However, the existing automatic expression identification algorithms cannot recognize expressions ideally, due to the small sample set, and the lack of diverse storage forms. To solve the problem, this paper explores the expression identification and emotional classification of students in job interviews based on image processing. Firstly, the ideas of interview emotion identification were expounded based on computer technology and image processing technology, and the college students’ interview emotion regulation process was modeled. Then, the histogram of oriented gradients (HOG) was adopted to extract the local textures and edges from the expression images of students in job interviews, and the face expressions were identified for the analysis on interview emotions. Based on the graph neural network (GNN) and representation learning, a job interview expression identification algorithm was designed for college students, which effectively suppresses the uncertainty of these images in the real-world unconstrained environments. The proposed algorithm was proved effective through experiments.