## 2 Related Work
### Visible Light Facial Expression Recognition  
Facial expression recognition under natural light is more mature and widely developed than in near-infrared environments. Facial expression recognition under natural light has been widely researched and developed because it is more in line with the needs of practical applications. Traditional expression recognition methods are usually based on image processing or machine learning techniques, in which relevant features in the image, such as texture, shape, etc., are manually selected or automatically extracted to describe the facial expression features, and then the extracted features are classified using traditional classifiers, such as support vector machines or decision trees. Such an approach often performs poorly on field datasets with light variations and diverse facial poses. With the development of deep learning, models based on convolutional networks are gradually becoming the mainstream framework for facial expression recognition. Convolutional networks automatically learn features such as edges and textures in an image by capturing information from local regions through convolutional operations and gradually extract higher-level features by stacking multiple convolutional layers, as well as using pooling to maintain more important features while reducing dimensionality and computational complexity, which has led to significant results in different computer vision tasks. A series of novel networks ([PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2019) have been proposed for general-purpose image classification tasks, which have also laid the foundation for facial expression recognition tasks. Considering the spatial localization of convolutional networks, researchers have proposed some methods to enhance the facial expression understanding of convolutional networks, such as the introduction of pyramid structure ([PERSON] et al., 2018) to reduce the loss of effective information due to the deeper model or multi-scale convolutional mechanisms ([PERSON] et al., 2018) to improve the ability to capture subtle changes in expressions. Some other researchers have used graph convolutional networks ([PERSON] et al., 2014; [PERSON] et al., 2015) to capture information about the expression features of dynamic sequences or to incorporate different attentional mechanisms depending on the task requirements, etc. [PERSON] et al. (2018) proposed an attention-based convolutional network focusing on different parts of the facial image to perform the FER task. [PERSON] et al. (2018) used a lattice attention mechanism in the initial learning phase to enhance feature learning for remote deviations between different facial regions. [PERSON] et al. (2019) improve the performance of recognition by proposing consistency loss to prevent the model from remembering noisy labels, making the model more able to focus on the valid parts. [PERSON] et al. (2019) combine three classical classification networks for feature fusion to cope with occlusion and pose change problems. [PERSON] et al. (2020) performed image segmentation in the middle of the network to cope with the problem of low-resolutionfacial expression recognition in the wild. [PERSON] et al. [31] adapted to a complex scene by constraining a joint multitasking network to assign global and local information weights. [PERSON] et al. [32] coped with the problem of occlusion by using Lanczos interpolation to maintain image quality and combined Hopfield and DBN networks for facial expression recognition tasks. In addition to this, several researchers have used neural network search [33] for micro-expression recognition. With ViT achieving very competitive results in image classification tasks and transformer-related models achieving more efficient performance on various computer vision tasks [34], some researchers have also introduced related model structures to the expression recognition task. [PERSON] et al.