Age and health appearance of 276 Chinese female volunteers were estimated from their photographs by 1025 female naïve Chinese graders 20–69 years old. Models were built to predict perceived age and health from topographic, colour and biophysical measured variables, in two subsets of the studied volunteers: below and above 50 years. Machine learning‐based predictive models for age and health perception were built on the collected data, and the interpretability of the models was established by measuring feature importance.
Age perception was mostly driven by topographic features, particularly eye bags and eyelid sagging in the group below 50 years old. Wrinkles, notably from the lower part of the face and oval of the lower face, were found to be more relevant in the group above 50 years. Health appearance was primarily signalled by skin imperfections and global pigmentation in the subset below 50 years, whereas colour‐related parameters and skin hydration acted as health cues for the subset above 50 years.
Published via International Journal of Cosmetics Science, July 4,2020
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