In this study, a non-touch online signature authentication system based on the MediaPipe framework is designed and implemented for the reliability and device dependency of handwritten signature authentication. The system utilizes MediaPipe as the underlying framework, captures the online handwritten signatures through video, extracts the temporal features of signature trajectory points as matching templates, and constructs a signature authentication model using a weighted joint probability strategy. The model achieves an average equal error rate (EER) of 3.04% on edge devices. An application designed based on PyQt is used as a visual UI interface to enable online non-touch signature authentication in video scenarios. This system uses real-time video sensing interaction to achieve online signature authentication without the need for other external devices, resulting in lower device dependency and greater authentication reliability.