## 2 Related Work  
In the past, this question has been addressed by several studies showing that the recognition of emotions expressed through the face is more difficult when the mouth is covered. A recent study suggested that this result was decreased because the mouth is responsible for most of the facial expressions of emotions and, therefore, when it is covered, there is less information to process. When most of the face is covered by a mask, the computer has a harder time understanding the facial expressions of the person [11; 12]. Machine learning (ML) algorithms can be used to automatically detect and classify facial expressions. These algorithms typically use a combination of features extracted from the face, such as the shape of the eyebrow, the size of the eye, and the position of the mouth. The accuracy of these algorithms varies depending on the data set used for training, but generally, they can achieve good accuracy [13; 14; 15]. Some common ML methods used for emotion recognition include support vector machines (SVM), Linear Discriminant Analysis (LDA), k-nearest neighbors (k-NN), and decision trees (DT). These methods can be used to learn models from data that can be used to recognize facial emotions. Neural Network also have an important impact on this area of research [16]. Deep learning architectures are like special tools that can be used to achieve specific goals. Each architecture has its own advantages and disadvantages, so it is important to select the right one for the task at hand [17]. CNN is very effective for emotion recognition tasks [18]. They can extract features from input images, and then use these features to train a classifier. Once the classifier is trained, it can be used to recognize images. The advantage of using a CNN is that it is able to learn complex patterns in input images. This is important because it enables CNN to recognize images [19; 20; 21; 22; 23; 24]. Recently, transfer learning methods have been widely used for prediction and classification. Transfer learning is a type of ML where knowledge gained from solving one problem is applied to a different but related problem. It is also an optimization technique that reduces the amount of training data required to solve a new ML problem [25]. The basic goal of transfer learning is to use the skills learned from one problem's resolution to aid in the solution of a related problem. This is especially helpful if you only have a small amount of data at your disposal. Numerous applications of transfer learning, including image classification, natural language processing, and even reinforcement learning, have been demonstrated to be successful [26]. Emotion recognition under a mask is difficult for ML algorithms [27] because there is often a lot of noise in the data that can make it difficult for computers to accurately recognize emotions. Additionally, it is difficult to accurately predict facial emotions when most of the face is covered by a mask [28]. As a result, the accuracy of emotion recognition decreases when compared with datasets that do not cover faces with masks. There are various reasons for this decreased accuracy, including the loss of important facial features under the mask, such as the mouth and eyes. In addition, the mask can distort the shape of the face, making it difficult for the algorithm to correctly identify facial features. This can impact the accuracy of the algorithms, as they are not able to see the full range of emotions that people are expressing. This can be a problem in applications such as security, healthcare, advertisement, education, and robotics where facial recognition is used to identify people's expressions. It is possible to improve the accuracy of emotion recognition under a mask by using a deep learning approach. One approach is to use a deep learning algorithm to learn to extract features from images and apply a different regression technique of ML to predict face-masked expression.