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

Wenkai Niu, Juxiang Zhou, Jiabeizi He, Jianhou Gan
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引用次数: 1

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.
基于SCN的课堂表情识别不确定性优化算法
随着面部表情识别(FER)技术在各个领域的逐步应用,基于特定场景的面部表情数据集逐渐增多,有效地提高了应用效果。然而,在真实课堂场景中采集到的学生面部图像往往存在前后遮挡、图像模糊、目标小等问题。此外,由于样本的不确定性,目前的学生课堂表情识别技术面临着一些挑战。因此,本文提出了一种基于SCN的不确定性优化算法。通过样本权值计算样本的修正权值,并根据修正权值设计损失函数。将噪声重标注模块中的阈值与修正权值相结合,得到动态阈值。在公共数据集和自建课堂表情数据集上的实验结果表明,优化算法有效地提高了SCN对不确定样本的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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