## 2 Related Work  
Three basic steps are usually involved in facial expression recognition: face detection, feature extraction, and classification. Among these steps, feature extraction is a crucial component in facial expression recognition. Existing feature extraction methods primarily include traditional approaches and deep learning methods.  
Traditional facial expression recognition methods employ handcrafted feature extractors, such as Gabor filters and Local Binary Patterns (LBPs). For instance, [PERSON] et al. [24] proposed a method that utilizes Gabor wavelets and Learning Vector Quantization (LVQ) for facial expression recognition. They extracted features using Gabor filters and combined them with LVQ for expression classification. [PERSON] et al. [25] combined LBP features with the AdaBoost algorithm for facial expression image representation. Another approach by [PERSON] et al. [26] involved using an Active Shape Model (ASM) and Support Vector Machines (SVMs) for facial expression recognition, investigating its accuracy and effectiveness. [PERSON] et al. [27] fused feature space algorithms with Principal Component Analysis (PCA), achieving good performance in facial expression recognition.  
Traditional image feature extraction algorithms heavily rely on handcrafted features, which may lead to the destruction of deep feature information that is essential for correctly classifying images. As a result, FER techniques based on deep learning have become available and shown exceptional recognition performance. These deep-learning-based methods offer more feature extraction flexibility than manually designed approaches. They are capable of extracting high-level features from images, thereby contributing to improved accuracy in facial expression recognition.  
Over the years, significant progress has been made in the development of deep neural network methods for facial expression feature extraction. For instance, [PERSON] et al. [28] proposed utilizing a pre-trained VGG16 facial expression recognition network, as introduced by [PERSON] et al. [29], as a feature extraction network. The extracted facial expression features were then inputted into an LSTM network. Furthermore, [PERSON] et al. [30] introduced a novel feature disentanglement model called SwapGAN (Generative Adversarial Network) for facial expression recognition. This method achieves a high degree of separation between facial-expression-related features and expression-invariant features, effectively overcoming the interference caused by individual differences in the facial expression recognition process. [PERSON] et al. [31] proposed a dual-channel network based on the Canny edge detector, which utilizes both the original image network and the edge image network to extract features without the need for additional redundant network layers or training. [PERSON] et al. [32] introduced a novel Erasing Attention Consistency (EAC) method that automatically suppresses noisy samples during the training process. Specifically, they designed an imbalanced framework based on the flip semantic consistency of facial images. Subsequently, they randomly erased a portion of the input images and employed flip attention consistency to prevent the model from excessively focusing on specific features.