Gui-Fen Zeng

Papers

Total Citations

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About

Gui-Fen Zeng is a researcher whose work sits at the intersection of traditional medicine and modern computer vision. Her primary research focus is the development of intelligent, automated methods for facial acupoint localization, a critical step in advancing the precision and accessibility of acupuncture and related therapies. Her most notable contribution is the "Facial acupoint location method based on Faster PFLD," a 2023 study that has already garnered 5 citations. This work introduces a deep learning framework that significantly improves the speed and accuracy of identifying key acupoints on the face, addressing a long-standing challenge in the field. By adapting the lightweight PFLD (Pose-invariant Face Landmark Detection) algorithm, Zeng’s method offers a practical, real-time solution for clinical and research settings. This achievement not only demonstrates her ability to bridge the gap between ancient medical practices and cutting-edge AI but also lays a robust foundation for future innovations in non-invasive diagnostics and treatment planning. Her work is particularly valuable for students and researchers exploring the intersection of healthcare and artificial intelligence.