## 2 Related Works  
Recent advancements in deep learning and computer vision have significantly impacted the field of Facial Emotion Recognition (FER). This section reviews various studies that have employed deep learning techniques to enhance the performance of FER systems, particularly those evaluated using the FER-2013 dataset. Proposed in reference [11], [PERSON] et al. presented an approach where features learned automatically by a number of CNN architectures are merged with handcrafted features through the bag-of-visual-words (BOVW) model. Emotion classification is achieved via this hybrid methodology, adopting a local learning framework and depicting a better performance in terms of accuracy as a result of the combined use of both automatic and handcrafted feature sets. [PERSON] et al. [12] developed a Local Multi-Head Channel (LHC) self-attention module referred to as a complementary component to the standard convolutional neural network (CNN). This mechanism avoids the generic actions of attention and concentrates instead on those parts of the face that are key actors in emotional expression. It was demonstrated that if you introduce LHC modules, you can boost the accuracy of facial expression recognition by localizing critical areas on the face. [PERSON] et al. [13] introduced Adaptive Correlation (Ad-Corre) Loss, which consists of three elements including Feature Discriminator, Mean Discriminator, and Embedding Discriminator. Ad-Corre Loss is used in conjunction with usual Cross-Entropy Loss to increase the discriminative capabilities of feature vectors, which consequently results in better accuracy of classification. This new loss function was experimented on Xception as well as Resnet50 models, and the outcomes showed major enhancements in performance. A novel FER model was proposed by [PERSON] et al. [14], which is a fusion of U-Net-based segmentation blocks into the VGG-19 architecture. This unique approach, termed Segmentation VGG-19, lays emphasis on significant features from a feature map, which in turn produces a better outcome: more advanced feature extraction and higher accuracy in recognizing emotions. [PERSON] et al. [15] presented a deep-learning-based FER method leveraging textural features such as local binary patterns (LBPs), local ternary patterns (LTPs), and completed local binary patterns (CLBPs). By training a CNN on these textural features, the model achieved improved accuracy in detecting facial emotions. [PERSON] et al. [16] proposed the ZFER method, which is based on zoning and improves the recognition of emotions on the face by providing additional landmarks including the eyes, eyebrows, nose, forehead, and mouth. This approach uses the hybrid VGG-16 model that learns based on zone-based landmarks to increase the accuracy of classifying facial emotions. [PERSON] and [PERSON] [17] put forward EmoNeXt, which is a modified ConvNeXt design specifically for FER. EmoNeXt includes an STN (Spatial Transformer Network) to concentrate on feature-rich areas on the face alongside Squeeze-and-Excitation blocks that allow for capturing channel-wise dependencies. Moreover, they added a self-attention regularization term that aims to generate feature vectors efficiently by compacting them. Results based on experiments carried out using the FER-2013 dataset showed that EmoNeXt is better than all existing state-of-the-art models when it comes to emotion classification accuracy. The dataset FER-2013, highly applied in these works, includes 35,887 gray-level images that are classified into seven emotional classes as follows: anger, disgust, fear, happiness, neutrality, sadness, and surprise. Its large volume and diversity of data make this dataset a standard reference point for FER system assessment [18]. The combination of these advanced deep learning methods (including self-attention mechanisms and hybrid models that have both automatic and handcrafted features) plus novel loss functions have greatly improved the performance of systems designed for facial expression recognition.