## 2 Related Works
used EfficientNet-b0 for feature extraction and transfer learning due to its accuracy and computational efficiency balance [65]. They customized the EfficientNet-b0 architecture by incorporating Swish activation functions after every 2D convolution layer, enhancing performance through non-monotonic, smooth unbounded above/bounded below properties. The researchers assessed the effectiveness of their model across five varied datasets: CK+, JAFFE, FER-2013, KDEF, and FERG. They reported classification accuracies of 100%, 95.02%, 63.4%, 88.3%, and 100% respectively for these datasets. [PERSON] et al. proposed a system design that utilizes convolutional neural networks (CNNs) with the OpenCV library to predict and classify facial emotions in real time [66]. Implemented on Raspberry Pi, the system comprises three main processes: face detection, facial feature extraction, and emotion classification. The Xception model achieved a prediction accuracy of 65.97% on the FER-2013 dataset for facial expression recognition. [PERSON] et al. introduced a deep learning approach employing attentional convolutional networks for facial expression recognition, surpassing previous models on various datasets, including FER-2013, CK+, FERG, and JAFFE, achieving accuracies of 70.02%, 98%, 99.3%, and 92.8%, respectively [67]. [PERSON] et al. introduced a novel deep neural network-based system for the early detection of cognitive impairment by analyzing the evolution of facial emotions in response to video stimuli. The system incorporates a facial expression recognition algorithm using layers from MobileNet and a Support Vector Machine (SVM), demonstrating satisfactory performances across three datasets like the KDEF dataset, Chinese Adults Dataset, and Chinese Elderly People Dataset [68]. A significant amount of work has focused on employing transfer learning techniques with CNN models such as AlexNet [69], SqueezeNet [70], and VGG19, evaluating their efficacy on benchmark datasets including FER2013, JAFFE, KDEF, CK+, SFEW [71], and KMU-FED. VGG19 demonstrated a notable performance, achieving 99.7% accuracy on the KMU-FED database and competitive results across other benchmark datasets. Specifically, VGG19 attained performance accuracies of 98.98% for the CK+ dataset, 92.99% for the KDEF dataset with all data variations, 91.5% for the selected KDEF Frontal View dataset, 84.38% for JAFFE, 66.58% for FER2013, and 56.02% for SFEW [72]. [PERSON] et al. explored emotion recognition through convolutional neural networks (CNNs), proposing various models including custom and transfer learning types, as well as ensemble approaches, alongside FER2013 dataset modifications. Emotion classification has been examined using both multi-class and binary approaches, with results and comparative analyses provided for the different methods and models [22]. In [73], the authors proposed a method for facial expression recognition by concatenating spatial pyramid Zernike moment-based shape features with [PERSON]'s texture features, capturing both macro and micro details of facial expressions. Using multilayer perceptron (MLP) and radial basis function feed-forward artificial neural networks, the method achieved a high recognition accuracy, with average rates of 95.86% and 88.87% on the JAFFE and KDEF datasets, respectively.