In the rapidly evolving landscape of artificial intelligence, the demand for smart and personalized human-machine interaction has grown. Facial expression recognition has emerged as a crucial research direction, becoming a hot topic in recent years. Traditional facial expression recognition algorithms require manual feature extraction, which can be complex and sometimes inefficient. In contrast, approaches based on deep learning leverage neural networks to construct end-to-end models that automatically extract features. The static facial emotion recognition technique using deep convolutional neural networks (CNNs) is currently a mainstream research direction due to its high classification accuracy and strong generalization capabilities. The facial expression recognition algorithm proposed in this paper utilizes a DenseNet (Dense CNN) model trained to extract features and classify facial expressions. The development is carried out using PyCharm software, incorporating PyQt5 and OpenCV modules to design a graphical user interface for the facial expression recognition system. This system not only meets the requirements for facial expression recognition in image and video files but also facilitates real-time facial expression recognition through a camera.