基于表情识别技术的学生情绪状态研究——以初中课堂为例

Research on Students’ Emotional State Based on Expression Recognition Technology——Taking the Junior Middle School Classroom as an Example

为了及时、客观、准确地在课堂上了解每个学生的听课状态,采用一种全局多尺度和局部注意力网络(MA-Net)的表情识别模型,具体来说,模型由三个部分组成:图像预处理及特征提取、全局多尺度模块和局部注意力模块。图像预处理提高输入图像的质量要求,核主成分分析进行特征提取,全局多尺度模块融合不同感受野的特征,降低深度卷积对遮挡和非正面姿态的敏感性,而局部注意力模块可以引导网络专注于局部显著特征,同时定义一个circle损失函数以规范整个学习过程,模型在FED-RO测试集上得到了较高的准确率。

In order to timely,objectively and accurately understand the listening status of each student in the classroom,a global multi-scale and local attention network (MA-Net) expression recognition model is adopted.Specifically,the model consists of three parts:image preprocessing and feature extraction,global multi-scale module and local attention module.The image preprocessing improves the quality requirements of input images,the kernel principal component analysis performs feature extraction,the global multi-scale module fuses features from different receptive fields,and reduces the sensitivity of deep convolution to the occlusion and non-frontal poses.The local attention module can guide the network to focus on local salient features,at the same time,it defines a circle loss function to standardize the entire learning process,and the model obtains a higher accuracy rate on the FED-RO test set.