An Optimization Algorithm for the Uncertainties of Classroom Expression Recognition Based on SCN

Abstract

With the gradual application of facial expression recognition (FER) technology in various fields, the facial expression datasets based on specific scenes have gradually increased, effectively improving the application effect. However, the facial images of students collected in real classroom scenes often have problems, such as front and rear occlusion, blurred images, and small targets. Moreover, the current students' classroom expression recognition technology faces several challenges as a result of sample uncertainties. Therefore, this paper proposes an optimization algorithm for the uncertainties based on SCN. The correction weight of the sample through the sample weight was calculated, and the loss function was designed according to the correction weight. The dynamic threshold is obtained by combining the threshold in the noise relabeling module and the correction weight. The experimental results on public datasets and self-built classroom expression dataset show that the optimization algorithm effectively improves the robustness of SCN to uncertain samples.