## 2 Related Works  
### Neural Network Models for Facial Emotion Analysis  
Facial landmarks can provide various information about a person's face. There are existing studies that classify human facial expressions using landmarks. In one study, the distances between facial landmarks were calculated to extract features and analyze relationships, classifying expressions into five categories [1]. Another study proposed an algorithm that utilizes a landmark-based ensemble network to classify expressions. This method uses landmark information from facial images to classify expressions into four emotional states through ensemble learning [2]. Various methods for extracting landmarks, which involve obtaining coordinates at specific locations on the face, have been researched. Extracting landmarks enables the recognition of faces in videos, which forms the foundation for technologies such as automatic facial focusing during video recording, detecting facial temperatures with thermal cameras, and inferring emotions from facial expressions [3].  
With recent advances in deep learning, the performance of facial feature point detection methods has significantly improved. Although the heatmap regression approach is widely used as an efficient and powerful method, it has certain limitations. To address these, a method combining integral regression with the heatmap regression approach was proposed, which greatly improved landmark detection performance [4]. For real-time operation on mobile devices, a method was suggested to implement deep learning-based facial detection and facial landmark algorithms on mobile platforms without significantly altering the deep network structures originally designed for desktops [5].  
There have been various prior studies on emotion classification through facial expressions using CNNs. One study focused on the theme of emotion and healing, recognizing users' expressions using deep learning, and playing music to amplify the emotion corresponding to the detected expression. This study implemented a two-layer model based on the MNIST model, classifying expressions into three categories: happiness, sadness, and surprise [6]. Another study using CNNs addressed the limitations of existing expression databases by proposing a model that classifies expressions into 'neutral', 'happy','sad', 'angry','surprised', and 'disgusted' using high-quality, diverse databases [7].  
To compare the predictive performance of various CNN architectures, a study analyzed the performance of AlexNet, GoogLeNet, VGGNet, and ResNet. Each model was trained for 100 epochs, with accuracy assessed at each epoch. The results indicated that accuracy tends to increase as the depth of the CNN architecture increases [8]. There is also research on developing a new small-scale deep convolutional neural network model to classifysmall datasets of low-resolution images. This model uses only a fraction of the memory compared to existing deep convolutional neural networks but produces very similar results on the FER2013 and FERPlus datasets [9]. These studies demonstrate that the optimal model structure may vary depending on the resolution of the input data, the size of the dataset, and the labels to be classified.