重庆理工大学学报2025，Vol.39Issue(9)：90-97,8.DOI:10.3969/j.issn.1674-8425(z).2025.05.011
融合注意机制的多尺度自适应空洞卷积面部情感识别方法
Facial emotion recognition with multi-scale adaptive dilated convolutional model incorporating attention mechanisms
摘要
Abstract
To overcome the difficulty of associated feature extraction for facial discontinuous action units and to address the problem that different facial regions may introduce useless information with different degrees of influence on emotion recognition,this paper proposes a multi-scale adaptive dilated convolution model based on a Dual Branching Attention Mechanism-Adaptive Multi-Scale Dilated Convolution(DAM-ADCNN).The model first generates feature mappings through a two-branch attention mechanism to characterize the local and global distributions and association relationships of facial action units.Then,the key features of the facial discontinuous action units are extracted using multi-scale cavity convolution.Finally,an adaptive approach is employed to dynamically adjust the weights of the associated features at different scales to effectively reduce the interference of useless information.Experimental results on the DEAP and CK+datasets show the DAM-ADCNN model outperforms existing methods in the emotion recognitions.On the arousal and validity dimensions of the DEAP dataset,the model improves the recognition accuracy by 3.66%and 3.99%respectively.On the CK+dataset,it enhances the recognition accuracy by 3.93%.These results demonstrate the effectiveness of the DAM-ADCNN model in emotion recognition through facial expressions.关键词
面部情感识别/双分支注意力机制/空洞卷积/自适应权重Key words
facial emotion recognition/two-branch attention mechanism/dilated convolution/adaptive weighting分类
信息技术与安全科学引用本文复制引用
王春影,孟天宇,张震,葛雄心,杨继伟..融合注意机制的多尺度自适应空洞卷积面部情感识别方法[J].重庆理工大学学报,2025,39(9):90-97,8.基金项目
吉林省科技发展计划项目(20190302114GX) （20190302114GX）
