Face Age Detection Based on YOLOv8

Face age recognition systems play a significant role in modern intelligent technologies and human-computer interaction. They not only enhance the efficiency of age recognition but also provide strong technical support for personalized services, security monitoring, and user experience optimization. This paper introduces a face age recognition model based on the YOLOv8 deep learning framework. The model was trained using a large number of face images of different genders, age groups, and scenarios, enabling it to accurately identify age features of faces under various lighting conditions, angles, and backgrounds. Additionally, a face age recognition system with a UI interface was developed, which supports real-time detection of age groups and visually presents the recognition results through a graphical interface. Experiments on the self-selected YOLO dataset show that as the number of experiments increases, the loss value significantly decreases and the prediction accuracy increases, indicating that the model can accurately detect age to a certain extent.