## 3 Results
### Unimodal Data Approaches
#### 3.2.1 Unimodal Physical Approaches
It includes basic emotions such as happiness, surprise, sadness, anger, contempt, fear, and neutrality. FER2013 (Facial Expression Recognition 2013): Created in 2013, it was used in a Kaggle competition called the \"Facial Expression Recognition Challenge\". This database includes images of facial expressions in real-world situations. While it contains a vast amount of data, the quality of labels may vary as some images come from online sources. OMG-One-Minute Gradual-Emotion Dataset [51]: This relatively new dataset, published in 2018, focuses on representing emotions with gradually evolving facial expressions in one-minute video clips. It is designed to capture the transition of emotions and subtle changes in facial expressions over time. The labels represent the gradual evolution of emotions throughout the videos collected from a variety of YouTube channels. This dataset is used for training and evaluating emotion recognition algorithms to understand how  
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emotions manifest over time and how algorithms can recognize and track these emotional transitions in video sequences. AffectNet [35]: This is a database of approximately 1 million images used for facial emotion recognition. It was presented at the 2017 IEEE International Conference on Automatic Face and Gesture Recognition. It contains a wide variety of subjects, with diversity in races, ages, and genders. Each image is labeled with the emotion represented by the person's face. They use the model of primary emotions, such as happiness, sadness, anger, surprise, fear, disgust, and neutrality. The images were collected from online sources, meaning that specific emotions were not induced in a controlled environment. Instead, emotions were retrospectively labeled by human annotators who evaluated the images. It presents challenges, such as the variability of human emotions and inherent limitations in emotion labeling in images. These aspects can impact the accuracy of facial recognition models. In the facial emotion recognition studies reviewed, the predominant machine learning models include convolutional neural networks (CNNs), transformer-based and deep learning models, and long short-term memory (LSTM) networks. Additionally, transfer learning, ensemble methods, and hybrid models that combine various approaches also play a significant role. To illustrate how these common AI models are specifically tailored and applied to address unique challenges in facial emotion recognition, the following example highlights their distinct and context-specific applications within this research domain:  
In their research, [PERSON] et al. [52] present an advanced technique for facial emotion detection, organized into three phases: super-resolution, emotion recognition, and classification. This approach employs an improved Deep CNN, the Viola-Jones algorithm, and various feature extraction methods to enhance image quality and emotion detection accuracy. The classification phase utilizes RNN and Bi-GRU networks with a score level fusion mechanism, outperforming traditional methods. On the other hand, [PERSON] et al. [53] introduce a new method to improve facial expression recognition when the training and testing images come from different sources, which is often difficult because of differences in the images. The method uses transfer learning by combining labeled images from one source with unlabeled images from another source to learn a common feature space and predict labels for the unlabeled images using a transductive transfer regularized least-squares regression (TTRLSR) model. After this, a SVM classifier is trained on these images to classify expressions in the target images. The method also uses color features from key points on the face to improve accuracy. Tests on two different databases show that this new approach, which integrates TTRLSR and SVM, performs better than current methods. Table 5 lists the trends in applications and focus areas in facial emotion recognition studies included in this review.