## 2 Related Works  
Numerous studies conducted in recent years have focused on FER, employing various techniques. Traditional machine learning approaches have been used alongside CNN models to obtain important information for classifying emotions extracted from visual objects. [PERSON] et al. [28] employed an ensemble approach using Wavelet Energy Features (WEFs) and Fisher's Linear Discriminants (FLD) for the feature extraction and classification of seven facial expressions (anger, disgust, fear, happiness, neutral, sadness, surprise) within the Japanese Female Facial Expression (JAFFE) dataset [29]. [PERSON] et al. utilized the Pyramid of Histogram of Gradients (PHOG) [30] and Local Phase Quantization (LPQ) [31] features to encode shape and appearance information. They selected keyframes through the K-means clustering [32] of normalized shape vectors from Constrained Local Models (CLMs) based face tracking. Emotion classification on the SSPNET [33] and GEMEP-FERA [34] datasets was conducted using an SVM and the Largest Margin Nearest Neighbor (LMNN) algorithm [35]. [PERSON] et al. proposed a framework employing two-fold RF classifiers to recognize Action Units (AUs) from image sequences. Facial motion measurements involved tracking Active Appearance Model (AAM) [36] facial feature points with Lucas-Kanade optical flow [37], using displacement vectors between the neutral and peak expressions as motion features. These features were fed into a first-level RF for AU determination, followed by a second-level RF for facial expression classification [38]. [PERSON] et al. focused on spatio-temporal feature extraction based on tracked facial landmarks, aiming to develop an automatic emotion recognition system [39]. They employed the KDEF dataset to identify features that represent different human facial expressions, subsequently evaluating them through various classification methods. Through experimentation and employing K-fold cross-validation, they achieved the precise recognition of facial expressions, attaining up to 87% accuracy with the newly devised features and a multiclass SVM classifier. [PERSON] et al. proposed five feature characteristics for FER and compared their performances using different classifiers and datasets (KDEF, CK+, JAFFE, and MUG [40]). Among Gaussian-based filtering and response (GABOR) methods, Haar [41], LBP, and histogram of oriented gradients (HOG) [42] classifiers, HOG classifiers perform best for FER with higher image resolutions (above 48 \(\times\) 48 pixels), averaging an 80% accuracy in these datasets [43]. The researchers found that the most straightforward method for classifying emotions is through CNN models. CNNs are well suited for image tasks due to their ability to capture various levels of features efficiently and recognize patterns and objects in images regardless of their positions or sizes. [PERSON] et al. used several classifiers such as ConvNet, RF classifiers, and Extreme Gradient Boosting (XGBoost) classifiers [44], with the CNN model ConvNet consistently yielding the highest accuracy in emotion classification [45]. The researchers proposed a novel FER approach that integrates a CNN with image edge detection to bypass traditional feature extraction. This method involves normalizing facial images, extracting edges, and merging this information with features to preserve the structural composition. Subsequently, implicit features are reduced using maximum pooling, followed by a softmax classification for emotion recognition. Testing on the FER2013 [46] and LFW datasets [47] resulted in an average emotion detection rate of 88.56% with faster training, approximately 1.5 times quicker than comparative models [48]. [PERSON] et al. focused on real-time FER, employing MobileNet [49] to train their model, achieving an 85% recognition accuracy for four emotions (happy, sad, surprise, disgust) on their custom dataset [50].