# Update class center using momentum-based smoothing
## 3 Experiments and Results  
### Data Set Details  
The algorithms in the paper are experimentally validated on three public data face expression sets FER2013, CK+ [41] and RAF-DB [42]. The FER2013 (Facial Expression Recognition 2013) dataset is a widely used dataset for facial expression recognition research. The FER2013 dataset was presented by [PERSON] and [PERSON] at the 2013 ICML (International Conference on Machine Learning) conference. It contains pixel data of face images labeled with 7 basic expression categories. The number of images in the FER2013 dataset is 35,887 grayscale face images, each with a size of 48 \(\times\) 48 pixels, with a total of seven basic expressions. The FER2013 dataset is divided into three parts, training set: 28,709 images, validation set: 3589 images, and test set: 3589 images. The dataset is provided as a CSV file. Each row of data contains the following information: emotion: expression label (0 to 6, corresponding to 7 expressions), Usage: identifies whether the image belongs to the training set, validation set, or test set (with the value of Training, PublicTest, or PrivateTest), pixels: pixel values of the image, stored as strings, with each pixel value ranging from 0 to 255, each image has 48 \(\times\) 48 pixel values, and the example data is shown in the Figure 8 (the pixel values of each image are only listed in the first few). The images in the FER2013 dataset are collected from the Internet and contain different lighting conditions, poses, and occlusions. So it is more difficult to recognize them, and there is a category imbalance problem, with a smaller number of samples for some expression categories (e.g., \"Disgust\" category), and all images are grayscale with no color information. The CK+ (Extended Cohn-Kanade) dataset is one of the most commonly used benchmark datasets in the field of facial expression recognition, developed by the team of [PERSON] and [PERSON] at the University of Pittsburgh, and subsequently extended by [PERSON] et al. The original CK dataset was released in 2000, and the extended version, CK+, was released in 2010. The CK+ dataset contains 123 subjects with a total of 593 video sequences of facial expressions, each starting with a neutral expression and progressively transitioning to a peak expression, labeled with seven basic expressions (anger, disgust, fear, happiness, sadness, surprise, and contempt). The image resolutions in the CK+ dataset are 640 \(\times\) 490 or 640 \(\times\) 480 pixels and the images are in grayscale format. Due to the high quality of the annotations and the relatively large size of the CK+ dataset, it serves as a benchmark when evaluating new methods. In addition, the dataset is also suitable for studying the link between facial expressions and internal emotional states. The CK+ dataset also has some limitations, such as the limited diversity of participants and the fact that all data were collected in a controlled environment, which may limit the model's generalization ability in more natural environments. Several sample images of the CK+ dataset is shown in the Figure 9.  
The RAF-DB dataset was developed by the Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS) in 2017, with images sourced from the Internet covering different races, ages, lighting conditions, and postures, and containing a total of 29,672 single-labeled emoji images and multi-labeled emoji images. The single-labeled images contain seven types of expression (anger, disgust, fear, happiness, sadness, surprise, and neutral), totaling 15,339 images, which were selected as the dataset. In the single-label image set of RAF-DB dataset, there are 12,271 images in the training set and 3068 images in the test set. Some sample images are shown in Figure 10.  
Figure 8: Example of image data from the FER2013 dataset. Figure 9: CK+ dataset sample images.