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一个学生的面部表情识别模型基于多尺度和深细粒度的特征注意力增强

Zhaoyu Shou^1,2, Yi Huang¹, Dongxu Li¹

¹School of Information and Communication, Guilin University of Electronic Science Technology, Guilin 541004, China.

Sensors (Basel, Switzerland)

|October 26, 2024

概括

这项研究引入了一个新的学生面部表情识别模型 (SFER-MDFAE) 用于智能教室. 该模型增强了特征提取,以更准确地识别学生的情绪和学习状态.

科学领域:

人工智能的人工智能
计算机视觉计算机视觉
教育技术的教育技术

背景情况:

准确的学生面部表情识别对于智能教室的适应性教学至关重要.
现有的方法在课堂环境中难以精确地提取面部特征和强度.

研究的目的:

开发一个先进的学生面部表情识别模型 (SFER-MDFAE).
在智能课堂环境中提高面部表情分析的准确性和稳定性.

主要方法:

提出了一个多尺度的双聚合特征聚合模块,用于全面捕获面部信息.
设计了一个以关键区域为导向的注意力机制,以增强细粒度的面部表情特征.
融合多尺度和注意力增强功能,以改善面部关键信息表示.

主要成果:

在基准数据集上实现了高准确度:76.18% (FER2013),92.75% (FERPlus),92.93% (RAF-DB),67.86% (AffectNet). 在基准数据集上实现了高准确度:76.18% (FER2013),92.75% (FERPlus),92.93% (RAF-DB),67.86% (AffectNet).
在真正的智能课堂数据集 (SCFED) 上表现出卓越的性能,准确度为93.74%.
在面部表情识别任务中超越现有的最先进方法.

结论:

SFER-MDFAE模型有效地解决了面部特征提取和识别强度的局限性.
拟议的方法显著提高了智能教室中学生面部表情识别的准确性.
通过对各种数据集的全面实验验证实有效性.

关键词:

深层细粒度的特征具有深层细粒度的特征.面部表情识别面部表情识别关键的面向区域的重点关注机制.多个尺度的特征是多个尺度的特征.智能课堂是一个智能课堂.

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