The ability of transformers to capture global context information is highly beneficial for recognizing subtle differences in facial expressions. However, compared to convolutional neural networks, transformers require the computation of dependencies between each element and all other elements, leading to high computational complexity. Additionally, the large number of model parameters need extensive data for training so as to avoid overfitting. In this paper, according to the characteristics of facial expression recognition tasks, we made targeted improvements to the Swin transformer network. The proposed Swin-Fer network adopts the fusion strategy from the middle layer to deeper layers and employs a method of data dimension conversion to make the network perceive more spatial dimension information. Furthermore, we also integrated a mean module, a split module, and a group convolution strategy to effectively control the number of parameters. On the Fer2013 dataset, an in-the-wild dataset, Swin-Fer achieved an accuracy of 71.11%. On the CK+ dataset, an in-the-lab dataset, the accuracy reached 100%.

Facial expression recognition (FER) is an important field in computer vision with many practical applications. However, one of the challenges in FER is dealing with small sample data, where the number of samples available for training machine learning algorithms is limited. To address this issue, a domain adaptive learning strategy is proposed in this paper. The approach uses a public dataset with sufficient samples as the source domain and a small sample dataset as the target domain. Furthermore, the maximum mean discrepancy with kernel mean embedding is utilized to reduce the disparity between the source and target domain data samples, thereby enhancing expression recognition accuracy. The proposed Domain Adaptive Facial Expression Recognition (DA-FER) method integrates the SSPP module and Slice module to fuse expression features of different dimensions. Moreover, this method retains the regions of interest of the five senses to accomplish more discriminative feature extraction and improve the transfer learning capability of the network. Experimental results indicate that the proposed method can effectively enhance the performance of expression recognition. Specifically, when the self-collected Selfie-Expression dataset is used as the target domain, and the public datasets RAF-DB and Fer2013 are used as the source domain, the performance of expression recognition is improved to varying degrees, which demonstrates the effectiveness of this domain adaptive method.