Expression Recognition Algorithm Based on Fusion Features for Students’ Emotional Analysis on Art Education Platform

Expression recognition is a crucial aspect of emotion computing research, with significant value in distance education, human-computer interaction, and medical research. This study analyzes students’ emotions on an art education platform with a focus on facial expression recognition algorithms using static images. To overcome the limitation of single facial features, a feature fusion algorithm is proposed to weigh and fuse regional features. The improved Local Binary Patterns (LBP) algorithm enhances texture feature extraction by utilizing a new threshold to describe image pixel relationships. Additionally, the Histogram of Oriented Gradients (HOG) extracts edge features from the eyebrow and mouth areas, which are then fused with the texture feature of the face using optimal weighting coefficients. The fused features are classified and recognized using Support Vector Machine (SVM) classifiers. Comparative experiments on the JAFFE and CK+ datasets demonstrated that the fusion feature-based facial expression recognition algorithm outperformed individual algorithms, achieving a recognition rate improvement of 12.7% and 13.6%, respectively. These findings offer theoretical insights and support for emotional analysis in the context of students’ art education.