## 1 Introduction  
In the wake of the COVID-19 pandemic, online video communication has become widespread, and we have come to realize that facial expressions on the screen play a significant role in mutual interactions. In recent years, facial expression recognition has emerged as a critical determinant of danger because of the rapid advancements in artificial intelligence and computer vision. For instance, it is now used for detecting driver fatigue in automotive safety systems [1]. In the system, driver behavior is monitored through an in-car camera. The camera analyzes information such as head posture, eye movements, and facial expressions to detect the level of driver fatigue. In the medical field, emotion recognition has become a preventive measure for conditions such as depression and other mental health disorders. The system analyzes individuals' emotional states by detecting emotions. If it detects prolonged negative emotional states, the system will encourage or connect users to professional mental health resources.  
In the past, people primarily relied on experience or a combination of their cognitive understanding of others to judge emotions when receiving emotional signals. However, these methods often carried biases. Today, people are turning to objective machines for expression recognition. These models automatically extract facial features and classify emotional categories. [PERSON] et al. [2] combined VGG 16 and LBP to extract facial features and classify six emotion categories with _softmax_. [PERSON] et al. [3] aimed to extract useful facial features and designed two novel convolutional neural network architectures. Then, they also achieved a 69% accuracy result.  
Traditional facial expression recognition faces several challenges, including imbalanced data, privacy concerns, and limitations in handling only single-person facial images. For example, datasets containing human faces are often incomplete and limited due to privacy concerns. Additionally, the dataset also suffers from data sparsity due to the limited number of samples for specific emotion categories. Furthermore, existing facial emotion recognition systems are constrained by their ability to process only single-face images. Therefore, we adopted data augmentation to enhance the facial image and improve the data sparsity problem. Subsequently, a CNN with the Haar cascade classifier ensemble was employed as the backbone network for the method. While the CNN with the Haar cascade classifier approach may not necessarily yield the highest accuracy, it possesses the capability to perform real-time detection on multiple individuals. This makes it valuable in applications such as automatic attendance systems in classroom settings, negative emotion alerts for mental health patients, and surgical assessments for facial deformities. We propose a method for facial expression recognition with low-cost hardware. In this method, facial expression recognition is regarded as an emotion classification task. We augment the facial image data, utilize the Haar cascade classifier for face localization, and employ a convolutional neural network (CNN) for emotion classification.  
The main contributions of this article are as follows:  
1. Integrating the Haar cascade classifier and CNN model to enhance feature extraction and ensure real-time capabilities of the model.
2. Designing a facial expression recognition system in both single-person and multi-person images.  
The rest of this article is organized as follows: Section 2 reviews the previous research on facial expression recognition. Section 3 describes the proposed approach. Section 4 presents the experimental results and a discussion of these results. Finally, Section 5 concludes this article.