| Citation: |
LIU Runzhu, LONG Xiao. Evolution of Facial Measurement Technology and Its Prospects with the Development of Artificial Intelligence[J]. Medical Journal of Peking Union Medical College Hospital, 2024, 15(6): 1242-1252. DOI: 10.12290/xhyxzz.2024-0585
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Facial anthropometry has profound importance in clinical diagnosis and the recognition of abnormal facial features. With the development of anthropometry, facial anthropometry has emerged as an independent research field and is widely applied in disciplines such as plastic surgery and cranio-maxillofacial surgery. This paper reviews the evolution of facial anthropometry and discusses its future trends in the context of artificial intelligence (AI). Currently, 3D facial imaging technology can accurately capture and reconstruct the three-dimensional morphology of facial soft tissues, and enhance the precision and objectivity of measurements, thus becoming the new "gold standard" for facial anthropometry. It not only provides reference for disease diagnosis and surgical planning but also plays a crucial role in evaluating cosmetic outcomes and aging research. In recent years, AI technology has developed rapidly, enabling direct recognition of abnormal facial features. Although facial recognition systems based on two-dimensional images are relatively mature, they have to struggle to fully capture facial features as they are limited by the dimensionality of information. While three-dimensional image-based recognition boasts high accuracy, it faces challenges in the recognition and classification of abnormal facial features due to limitations in the number of training samples. The integration of AI and facial anthropometry has effectively promoted automatic recognition technology for facial landmarks, thus providing more precise and interpretable methods for assessing disease-related facial features. Future research should focus on building reliable three-dimensional facial databases to further improve the accuracy of facial recognition. Additionally, developing facial recognition systems based on small sample sizes is necessary to provide robust support for the recognition of facial features associated with rare and special diseases.
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