## 1 Introduction  
Facial expressions represent one of the most authentic and biologically significant channels for human emotional communication, providing direct insight into affective states. Facial expression recognition is a technology that analyzes and processes face images by computer to recognize the state of human facial expression [1]. In 1971, six basic facial expression categories were identified by famous psychologist [PERSON] [2]: Angry, Disgust, Fear, Happy, Sad, and Surprise. Neutral emojis have also been proposed as society continues to evolve, resulting in the current mainstream seven emoji states. Face Expression Recognition (FER) recognizes human facial expressions through several steps such as face detection, face alignment, feature extraction, and classification [3], and has been widely used in the fields of intelligent interaction, medical diagnosis, and security monitoring. In terms of intelligent interaction, by recognizing the user's facial expression, a more intelligent and natural interaction experience can be realized. In intelligent driving systems, facial expression recognition technology enables real-time monitoring of driver affective states, facilitating timely interventions to maintain alertness and thereby enhancing road safety. For instance, detection of fatigue or distraction patterns can trigger adaptive warning systems to prevent accidents. In addition, in the field of virtual reality and augmented reality, facial expression recognition technology can also realize a more immersive user experience, providing users with a richer and more vivid interactive experience [4]. In terms of medical diagnosis, by analyzing patients' facial expressions, doctors can more accurately understand their emotional state and mental health status, thereby assisting in diagnosis and treatment [5]. Additionally, facial expression recognition technology can also be used for postoperative rehabilitation monitoring. During the postoperative rehabilitation process, the patient's facial expressions can reflect their level of pain, comfort, and emotional state. By analyzing this information, doctors can adjust treatment plans in a timely manner and improve the effectiveness of rehabilitation therapy [6]. In the field of security and surveillance, face expression recognition technology has important application potentials [7]. By recognizing facial expressions in the surveillance area, the surveillance system can more accurately perceive the emotional state and behavioral intentions of the personnel, thus improving the intelligence and responsiveness of the surveillance system [8]. Currently, in the field of expression recognition research, the mainstream face expression recognition methods are mainly divided into traditional machine learning-based methods and deep learning-based methods. There are two main traditional machine learning methods for describing faces: geometric feature-based methods and texture feature-based methods. However, conventional machine learning methods often separate facial feature extraction from classification into distinct stages, resulting in complex operations and lack of joint optimization. The effectiveness of recognition depends on the discriminative power and robustness of manually designed features. The meteoric rise of deep learning technology has injected new vitality into machine learning-based expression recognition methods. Unlike traditional methods, in deep learning methods both the process of feature extraction and classification can be done by the deep learning model itself. The stunning debut of AlexNet [9] in 2012 provided improvement ideas for neural network development, allowing numerous network models such as VGG-Nets [10], GoogleNet [11], ResNet [12], and DenseNet [13] to employ deepening of the network and enhancement of convolutional functionality to improve network recognition accuracy.