## 1 Introduction  
Emotions play a significant role in human interactions, serving as essential mediators in social communication systems [1]. Humans' expression of emotions incorporates diverse modalities, including facial expressions, speech patterns [2], and body language [3]. According to [PERSON] and [PERSON] [4], human facial expressions indicate humans' emotional states and intentions. Recently, automatic emotion detection through computer vision techniques has shown a growth in interest and application across many domains, including hospital patient care [5], neuroscience research [6], smart home technologies [7], and even in cancer treatment [8, 9]. This diversity has established emotion recognition as a distinct and growing field in research fields, primarily due to its wide range of applications and intense impact on various phases of human life. Emotion recognition from images mainly consists of two steps: feature extraction and classification. Facial images encompass a multitude of features including geometric, texture, color, intensity, landmark, shape, and histogram-based features. Handcrafted techniques for feature extraction in facial images involve the manual identification of landmarks for geometric features, texture analysis using methods like Local Binary Patterns (LBPs) [10], and color distribution analysis through histograms. To enhance the feature extraction process, dimensionality reduction techniques such as PCA (Principal Component Analysis) [11]/t-SNE (t-Distributed Stochastic Neighbor Embedding) [12] have been employed to obtain crucial features for classification. Traditional machine learning algorithms like Support Vector Machine (SVM) [13] and Random Forest (RF) [14] have been used to classify the emotions from these features. However, hand-crafted features often struggle to capture the important information required for effective face identification. Moreover,kernel-based methods frequently produce feature vectors that are excessively large, leading to the overfitting of the model [15]. Deep learning models, particularly convolutional neural networks (CNNs) [16], are renowned for their capability to automatically learn hierarchical features and complex patterns. However, CNNs frequently face challenges such as overfitting, which arises from limited data availability and computational complexity. Additionally, issues like vanishing or exploding gradients can undermine the stability of the training processes [17]. Transfer learning has gained popularity in machine learning as a method for accelerating tasks. Transfer learning provides a framework for leveraging well-known pre-trained models, such as VGG (Visual Geometry Group) [18], ResNet [19], and DenseNet121 [20], trained on millions of image data and is particularly relevant for FER application in the related domain of facial images. A schematic representation of the proposed framework is presented in Figure 1, utilizing images sourced from the Karolinska Directed Emotional Faces (KDEF) [21] dataset for illustrative purposes. As presented in Figure 1, the first step (1) showcases facial images retrieved from the KDEF, Filtered Facial Expression Recognition 2013 (FER2013) [22], and Cohn-Kanade (CK+) [23] datasets. In the subsequent step, step (2), the data undergo preprocessing, wherein data augmentation techniques such as horizontal flipping, zooming, rotation, and histogram equalization [24] methods are applied. These techniques serve to augment the dataset, enhancing image contrast and thereby facilitating improved feature extraction. In moving forward to step (3), the fine-tuning and modification of the pre-trained VGG19 and VGG16 models are undertaken. During this process, the last convolutional block of the models is kept unfrozen, while the remaining layers of the base models (pre-trained VGG16, VGG19) are frozen. Additionally, the fully connected layers are then connected to these models. Moreover, diverse learning rate schedulers, including cosine annealing [25] are implemented in the models.