# HSAW: A Half-Face Self-Attention Weighted Approach for Facial Expression Recognition
## 1 Introduction  
Facial expressions are a fundamental aspect of human communication, offering critical insights into an individual's emotional state without needing verbal interaction. They are pivotal in social interactions, affecting perceptions, decisions, and behaviors of interpersonal relationships. Therefore, the automatic recognition of facial expressions holds significant promise for enhancing human-computer interaction, contributing to advancements in psychological research, and supporting various applications in the security and entertainment industries. However, accurately recognizing and interpreting facial expressions through computational methods poses a considerable challenge, primarily due to facial movements' complex and dynamic nature. One of the difficulties in facial expression recognition is the asymmetry of expressions. The reason for this is that the face is the most complex signaling system in the human body and a highly differentiated part of the body [1]. During conversations, [PERSON] et al. [2] systematically and subtly uncovered that the regions of the cerebral cortex responsible for emotional expressions demonstrate variations between the left and right halves of the face. Such asymmetries often manifested on half or part of the face, introduce discrepancies in expression features, leading to inaccuracies in automated recognition systems. This phenomenon reflects the diverse control mechanisms governing facial muscles and highlights the complexity underlying human emotional expression. This asymmetry poses a unique challenge to facial expression recognition techniques, as it requires distinguishing and accurately interpreting the features of expression asymmetry. However, asymmetric features may interfere with detection based on the wholeface, and the accuracy of facial expression recognition still needs to be further improved. Recent efforts have focused on leveraging local facial expression features to enhance facial expression recognition accuracy. [PERSON] et al. [3] employed a trained multi-channel convolutional neural network to extract features from localized regions such as the eyebrows, eyes and mouth. Subsequently, these local features are fed into a unified multi-scale fusion network to derive the results used for expression classification. [PERSON] et al. [4] introduced a regional attention network model to address the challenges associated with head deviation and facial occlusion. In their approach, localized regions in a given image are initially identified based on predefined criteria. These regional areas and the original image are then processed through a backbone network to extract features indicative of facial expressions. Ultimately, a classifier leverages the global features, features of the local regions, and their respective weights from the original image to determine the expression category. Although the above methods use local features to improve expression recognition accuracy, the different effects of the left and right half-face on expression classification still need to be further distinguished. To address the issue aforementioned, in this paper, we propose an expression recognition approach based on a self-attention weighted half-face. In this approach, facial region is divided into the left and right half-faces using a segmentation algorithm. Features are then extracted from each half-face separately using a neural network. Based on the observed asymmetry, appropriate weights are assigned to the left and right halves using a self-attention weighted module. This ensures that the expression features from each half-face uniquely influence the final decision. Consequently, the impact of information asymmetry on the results is minimized. In addition, due to the presence of uncertain classification labels in the dataset after the self-attentive weighting module, this paper introduces an adaptive re-labeling module, which aids in prediction through pseudo-labeling and improves the algorithm's accuracy.