Mou Xiangwei, Song Yongfu, Xie Xiuping, You Mingxuan, Wang Rijun
College of Electronic and Information Engineering/Integrated Circuits, Guangxi Normal University, Guilin 541004, China.
Teachers College for Vocational and Technical Education, Guangxi Normal University, Guilin 541004, China.
Sensors (Basel). 2025 Jun 19;25(12):3829. doi: 10.3390/s25123829.
Facial expressions involve dynamic changes, and facial expression recognition based on static images struggles to capture the temporal information inherent in these dynamic changes. The resultant degradation in real-world performance critically impedes the integration of facial expression recognition systems into intelligent sensing applications. Therefore, this paper proposes a facial expression recognition method for image sequences based on the fusion of dual neural networks (ResNet and residual bidirectional GRU-Res-RBG). The model proposed in this paper achieves recognition accuracies of 98.10% and 88.64% on the CK+ and Oulu-CASIA datasets, respectively. Moreover, the model has a parameter size of only 64.20 M. Compared to existing methods for image sequence-based facial expression recognition, the approach presented in this paper demonstrates certain advantages, indicating strong potential for future edge sensor deployment.
面部表情涉及动态变化，基于静态图像的面部表情识别难以捕捉这些动态变化中固有的时间信息。在实际应用中性能的下降严重阻碍了面部表情识别系统集成到智能传感应用中。因此，本文提出了一种基于双神经网络（ResNet和残差双向GRU-Res-RBG）融合的图像序列面部表情识别方法。本文提出的模型在CK+和Oulu-CASIA数据集上的识别准确率分别达到了98.10%和88.64%。此外，该模型的参数规模仅为64.20M。与现有的基于图像序列的面部表情识别方法相比，本文提出的方法具有一定优势，显示出未来在边缘传感器部署方面的强大潜力
