## 2 Related Works
[17] proposed a MixAugment data augmentation method based on Mixup. Mixup demonstrated an improvement in the recognition rate for wild data in the context of facial expression recognition tasks. [PERSON] et al. [18] introduced a novel facial emotion recognition model vision transformer (ViT) model. Recognizing the common issue of data imbalance for widely used emotion datasets, the researchers opted for the evaluation of their model on RAF-DB and FER-2013. Additionally, to address the imbalance challenge, they employed data augmentation techniques to construct a balanced dataset. A wavelet is a mathematical tool used in signal processing and image processing, frequently applied in applications such as facial recognition. Wavelets exhibit advantages in multi-scale analysis and feature extraction. The particular significance of real-time facial recognition is due to its feature compression properties. The Symlet wavelet, Coiflet wavelet, and Haar wavelet are commonly employed wavelet techniques in facial recognition. The Symlet wavelet is characterized by its symmetry and flexibility. With adjustable parameters, it provides improved accuracy in facial identification. The Coiflet wavelet is a family of compactly supported wavelets. Its finite support property effectively reduces computational complexity while preserving essential features. Therefore, it exhibits superior performance in both time and frequency domains. The Haar wavelet is the simplest form of a wavelet. Its straightforward computation gives it a significant advantage in real-time signal processing. While the Symlet wavelet and Coiflet wavelet can depict more detailed facial contour information, their computational demands make them less feasible in resource-constrained scenarios. Conversely, the Haar wavelet is unable to capture intricate facial contour details. However, its minimal resource requirement and precise edge detection capabilities contribute to its excellent performance in capturing facial positions. In our system, the prioritization of facial position detection outweighs the demand for capturing detailed facial contours. Therefore, we opted for the Haar wavelet to be incorporated as a part of our model. [PERSON] et al. [19] discussed various applications of Haar in different fields and categorized the proposed methods. [PERSON] et al. [20] employed an enhanced active shape model (ASM) for facial expression recognition, utilizing Haar features to automate face detection in their applications and research. [PERSON] et al. [21] proposed an Adaboost-based face detection algorithm using Haar-like features. The model aimed to improve issues related to extended training times and low detection efficiency. [PERSON] et al. [22] proposed a facial recognition system. The system combines Haar-like features, face detection, and OpenCV to compare certain facial features in the image with a facial dataset. [PERSON] et al. [23] proposeda CNN-enhanced multi-level Haar wavelet features fusion network (CNN-MHWF2N). The system combines the spatial features of the 2-D-CNN and the Haar wavelet decomposition features to obtain spectral and spatial information. [PERSON] et al. [24] proposed a face image recognition method. The model is based on Haar-like and Euclidean distance and improved accuracy of facial recognition. [PERSON] et al. [25] proposed a weak classifier that improved the Haar-like features and the AdaBoost algorithm. The weight update method in the AdaBoost algorithm was changed and achieved an effective accuracy improvement. [PERSON] et al. [26] presented a graph-based approach to emotion recognition. In their methodology, a two-step process was employed, combining the Haar cascade classifier and a media-pipe face mesh model. Initially, the Haar-cascade classifier was utilized for face detection, followed by the application of the media-pipe face mesh model for precise landmark localization. This synergistic combination allowed for the extraction of facial features critical for emotion recognition. Notably, the proposed framework by [PERSON] et al.