Abstract:

OBJECTIVE: To develop a method for accurate identification and localization of facial acupoints based on geometric and topological relationships (GTRs) among facial key points and organ features.

METHODS: A facial acupoint localization framework was constructed using the Google MediaPipe machine learning toolkit to extract 478 facial key points. Geometric and topological relationships between predefined acupoints and key facial landmarks were established. Based on these relationships, a rule-based mapping algorithm was designed to identify and localize facial acupoints. The method was applied to facial images collected from individuals undergoing acupuncture and physical therapy, and its localization performance was evaluated.

RESULTS: The proposed method successfully identified and localized facial acupoints based on stable geometric and topological relationships. The approach demonstrated consistent performance across different facial structures, enabling accurate positioning of acupoints without the need for large-scale annotated datasets. The results indicate that the method is feasible and reliable for practical applications.

CONCLUSION: The GTR-based approach provides an effective and efficient solution for facial acupoint identification and localization, reducing dependence on manual annotation and improving applicability in clinical and intelligent acupuncture systems.

Key words: acupoint recognition, MediaPipe, geometric topological relationships, key point extraction, deep learning