## 4 Experimental Results
### Datasets
#### 4.1.3 Our Balanced FER2013 Dataset  
As in other domains, one of the most crucial elements in enhancing the precision of facial emotion recognition models is a balanced dataset. In this study, we used the cleaning and augmentation techniques to build a new, balanced FER2013 dataset, which was based on the existing FER2013 dataset. The FER2013 dataset is a well-known benchmark for facial emotion recognition (FER) tasks. But there are some issues with the FER2013 dataset. Among the main challenges are:  
* Contrast variation: It is possible that the dataset contains photos that are either too bright or too dark. CNN models, which learn visual features automatically, tend to perform better with high-contrast images. Low-contrast images, on the other hand, may affect CNN performance due to the lower amount of information they transmit. This issue can be resolved by improving the quality of the faces in the pictures.
* Imbalance: When one class has many more photos than another, there is a class imbalance. This may skew the model in favour of the dominant class. The model will favour the cheerful class, for instance, if there are 100 photographs of joyful people and 20 images of afraid people. To address this issue, data augmentation techniques like horizontal flipping, cropping, and padding can be applied to increase the amount of data available for the minority classes [35].  
Figure 7: RAF-DB dataset samples.  
* Intra-class variation: A range of facial expressions, including animated faces and drawings, are included in the dataset. The features of real and animated faces differ, which can make it challenging for the model to extract landmark elements. In order to enhance model performance, only photographs of actual human faces should be included in the dataset.
* Occlusion: When part of the image is hidden, this is known as occlusion. This may occur when someone is wearing sunglasses or a mask, or when a hand covers a portion of the face, like the right eye or nose. Occluded photos should be eliminated from the dataset, since the eyes and nostrils are crucial characteristics for detecting and extracting emotions.  
The FER2013 dataset can be enhanced to become a more trustworthy standard for FER research by resolving these issues. In this work, we present a new dataset, FER2013_balanced, which is obtained by data augmentation methods, using the FER2013 dataset. Initially, we excluded poor-quality photos from the FER2013 dataset, including those with low contrast or occlusion. A depiction of poor-quality images from the FER2013 dataset is shown in Figure 8, arranged according to the relevant categories.  
Next, in order to enlarge the minority classes, we employed data augmentation techniques on the remaining images. Finally, we eliminated a few photos from the happy, neutral, sad, and other categories to balance our dataset. The FER2013_balanced dataset contains an equal number of photos for every emotion category. This ensures a balanced distribution of classes, reducing the possibility of bias towards the majority classes. Thus, it serves as a reliable baseline for FER research. The number of images in each category from the FER2013 and FER2013_balanced datasets is shown in Figure 9.  
Figure 8: Examples of poor-quality images sourced from the FER2013 database.