Background: Skin assessment methodologies have focused mainly on intuitive aging characteristics, including facial wrinkles and pigmented spots, and usually adopt pattern recognition algorithms. Recently, distinct methods of interpreting skin aging, such as the detection of facial landmarks and age prediction using machine learning techniques, have been conducted.
Materials and methods: We defined two indices that represent the severity of facial aging. The first index was the ratio of the bizygomatic distance and bigonial distance. The second index was the ratio of the degrees of the near mandible. The indices extracted from two-dimensional frontal face images were intended to show the deformation of the facial skin downward with aging progress. To validate whether these proposed indicators can represent facial aging, we conducted correlation tests with age and facial skin characteristics and performed association tests between the indices and facial skin characteristics, adjusted for age.
Results: The indices showed strong correlations with age (r = 0.557 and 0.464, respectively) and facial skin characteristics. Although there were correlations between the indices and facial skin features, the associations between the indices and facial skin characteristics adjusted for age were weak or not significant. This suggests that the newly developed indices are appropriate for evaluating facial skin aging and distinct from typical measurements.
Conclusion: We suggest two novel indices for evaluating facial aging based on frontal face images. The indices exhibited strong correlations with age and representative facial skin characteristics. The newly developed values can be differentiated indicators of facial aging compared with general skin features.
Keywords: Janus-Ⅲ measurement system; facial aging index; facial degree ratio; facial skin characteristic; facial width ratio.
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