面向学生课堂情感计算的自适应混合网络  

Adaptive hybrid network for affective computing in student classroom

作　　者：戎妍 刘嘉雯 李馨蕾 RONG Yan;LIU Jiawen;LI Xinlei(School of Statistics and Information,Shanghai University of International Business and Economics,Shanghai 201620,China;The Hong Kong University of Science and Technology(Guangzhou),Guangzhou Guangdong 511458,China)

机构地区：[1]上海对外经贸大学统计与信息学院,上海201620 [2]香港科技大学(广州),广州511458

出　　处：《计算机应用》2024年第9期2919-2930,共12页journal of Computer Applications

基　　金：上海市科委“科技创新行动计划”启明星项目(扬帆专项)(22YF1415000);上海市教委上海高校青年教师培养资助计划项目(B3A010023045001);国家级大学生创新创业训练计划项目(202210273042)。

摘　　要：情感计算可以为智慧教育提供更好的教学效果和学习体验。目前针对课堂领域的情感计算研究仍存在有限的适应性与对复杂场景的感知能力较弱的问题。针对这一挑战,提出一种混合架构SC-ACNet,旨在对学生课堂进行准确的情感计算。该架构包含一个能适应小目标的多尺度学生面部检测模块;一个能适应不同面部姿态的、具有自适应空间结构的情感计算模块,对学生的5种课堂情感(平静、困惑、愉悦、困倦和惊讶)进行准确识别;以及一个自注意力模块,以可视化模型中对结果产生主要贡献的区域。此外,为缓解课堂环境下学生面部情绪图像数据集匮乏的问题,构建了一个学生课堂数据集SC-ACD。在SC-ACD数据集上的实验结果表明,与基线方法YOLOv7相比,SC-ACNet的平均精度均值(mAP)提升了4.2个百分点,情感计算准确率提升了9.1个百分点;此外,SC-ACNet在KDEF和RaFD数据集上的准确率分别达到了0.972和0.994,验证了SC-ACNet可作为提高智慧课堂教学质量的有前途的解决方案。Affective computing can provide a better teaching effectiveness and learning experience for intelligent education.Current research on affective computing in classroom domain still suffers from limited adaptability and weak perception on complex scenarios.To address these challenges,a novel hybrid architecture was proposed,namely SC-ACNet,aiming at accurate affective computing for students in classroom.In the architecture,the followings were included:a multi-scale student face detection module capable of adapting to small targets,an affective computing module with an adaptive spatial structure that can adapt to different facial postures to recognize five emotions(calm,confused,jolly,sleepy,and surprised)of students in classroom,and a self-attention module that visualized the regions of the model contributing most to the results.In addition,a new student classroom dataset,SC-ACD,was constructed to alleviate the lack of face emotion image datasets in classroom.Experimental results on SC-ACD dataset show that SC-ACNet improves the mean Average Precision(mAP)by 4.2 percentage points and the accuracy of affective computing by 9.1 percentage points compared with the baseline method YOLOv7.Furthermore,SC-ACNet has the accuracies of 0.972 and 0.994 on common sentiment datasets,namely KDEF and RaFD,validating the viability of the proposed method as a promising solution to elevate the quality of teaching and learning in intelligent classroom.

关 键 词：情感计算 人脸检测 混合架构 智慧课堂 多尺度特征 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]