## 2 Related Works
Experimental validation across multiple databases and facial orientations resulted in significant findings: achieving an accuracy of 89.58% on the KDEF dataset, 100% accuracy on the JAFFE dataset, and 71.975% accuracy on the combined dataset (KDEF + JAFFE + SFEW). These results were obtained using cross-validation techniques to minimize bias. In [51], researchers explored visual emotion recognition through social media images by employing pre-trained VGG19, ResNet50V2, and DenseNet-121 architectures as their base. Through fine-tuning and regularization techniques, these models demonstrated improved performances on Twitter images from the Crowdflower dataset, with DenseNet-121 exhibiting superior accuracies of 73%, 75%, and 89%, respectively. Furthermore, [PERSON] et al. investigated dual transfer learning for facial emotion classification, experimenting with pre-trained CNN architectures including VGG16, ResNet50, Inception ResNet [52], Wide ResNet [53], and AlexNet. By combining extracted feature vectors into various pairs and inputting them into an SVM classifier, this approach showed promising results in terms of accuracy, kappa, and overall accuracy compared to state-of-the-art methods across benchmark datasets such as JAFFE, CK+, KDEF, and FER2013 [54]. [PERSON] et al. introduce FERFM, a novel approach using a fine-tuned MobileNetV2 [55] for FER on mobile devices. A pipeline strategy was introduced, where the pre-trained MobileNetV2 architecture is fine-tuned by eliminating the last six layers and adding a dropout, max pooling, and dense layer. Using transfer learning from ImageNet, the method achieved an accuracy of 85.7% on the RGB-KDEF dataset. It surpasses VGG16 with faster processing at 43 ms per image and fewer trainable parameters, totaling 1,510,599 [56]. In another research, they proposed a system that employs a CNN framework using AlexNet's features, achieving higher accuracy compared to other methods across various datasets like JAFFE, KDEF, CK+, FER2013, and AffectNet [57]. Moreover, they proved it is more efficient and requires fewer device resources than other state-of-the-art deep learning models like VGG16, GoogleNet [58], and ResNet [59]. In another study, [PERSON] et al. fine-tuned the VGG-Face Deep CNN model pre-trained for face recognition. The study investigated the impact of a cross-database approach [60]. The results revealed significant accuracy improvements, with average accuracies of 88.58%, 67.03%, 85.97%, and 72.55% on the CK+, MMI, RaFD, and KDEF databases, respectively. [PERSON] et al. proposed a hybrid facial expression recognition model combining image pre-processing and convolutional neural network (CNN) structures to enhance accuracy and reduce training time. Across various databases and facial orientations, the model achieved high accuracies, with notable results, including 100% accuracy for the JAFFE dataset and 89.58% accuracy for the KDEF dataset [61]. [PERSON] et al. introduced the Attentive Cascaded Network (ACD) method, which enhances the discriminative power of facial expression recognition models by selectively focusing on important feature elements [62]. By integrating multiple feature extractors with smooth center loss, ACD achieves intra-class compactness and inter-class separation, improving the generalization ability of the learning algorithm. In their experiment, the proposed method achieved a notable performance on the RAF-DB and KDEF datasets, with accuracies of 86.42% and 99.12%, respectively. In [63], the researchers introduced a novel approach to facial expression recognition by combining deep metric loss and softmax loss in a unified framework, enhancing the performance by addressing intra- and inter-class variations. Using a generalized adaptive (N+M)-tuplet cluster loss function and identity-aware mining schemes, the proposed method achieved an accuracy of approximately 97.1% on the CK+ dataset and 78.53% on the MMI dataset [64]. [PERSON] et al.