沈阳理工大学自动化与电气工程学院;
针对目前大多数面部表情提取未充分考虑语义特征以及个人独特面部特征导致面部表情识别准确性低的问题,提出一种基于特征解码的高效表情识别方法,称为FER-FD方法。该方法由两个模块组成,即特征解耦模块(FFD)和语义强化模块(VTS)。首先,FFD模块使用两个深度二维卷积神经网络从输入图像中提取面部和表情特征,面部特征解耦器将面部特征与表情特征解耦,以最大限度地减少个人独特面部特征的影响;其次,VTS模块采用两个关键思想以无监督的方式自动捕获面部运动,从而建立全局面部区域的深层语义信息;最后,将两个模块的特征串联起来,以更准确地预测样本的面部表情。实验结果表明,本文提出的特征解码方法在CK+数据集上获得了98.78%的准确率,对不同场景具有可扩展性和适应性,具有较好的泛化能力。
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基本信息:
DOI:
中图分类号:TP391.41
引用信息:
[1]吴东升,林玉婷,徐鹏飞.基于特征解码的表情识别方法研究[J].沈阳理工大学学报,2025,44(01):19-24.
基金信息:
辽宁省教育厅高等学校重点攻关项目(JYTZD2023006)