Messaraa C, Richard T J C, Walsh M, Doyle L, O'Connor C, Robertson N, Mansfield A, Hurley S, Mavon A, Grenz A
Oriflame R&D, Bray Business Park, Kilruddery, Bray, A98 Y6W0, Ireland.
Oriflame Skin Research Institute, Oriflame Cosmetics AB, Mäster Samuelsgatan 56, Stockholm, 11121, Sweden.
Int J Cosmet Sci. 2020 Oct;42(5):471-481. doi: 10.1111/ics.12647. Epub 2020 Aug 20.
BACKGROUND & AIMS: Previous investigations have aimed at investigating parameters affecting age perception on several ethnicities. Perceived health has been a newer focus on Caucasian skin, yet little is known on the skin features used to estimate the health status of Chinese women and we aimed to investigate whether these cues are the same as those used for age perception.
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.
Distinct skin features were acting as cues for age perception and/or health perception and varied per age subset. Their contribution should be borne in mind when designing products for 'younger looking skin' and 'healthier looking skin'.
以往的研究旨在探究影响多个种族年龄认知的参数。感知健康已成为白种人皮肤研究的一个新焦点，但对于用于评估中国女性健康状况的皮肤特征却知之甚少，我们旨在研究这些线索是否与用于年龄认知的线索相同。
1025名年龄在20至69岁之间的未受过专业训练的中国女性评分者根据276名中国女性志愿者的照片对其年龄和健康外观进行评估。在研究志愿者的两个子集中，根据地形、颜色和生物物理测量变量建立模型，以预测感知年龄和健康状况：50岁以下和50岁以上。基于收集的数据建立了基于机器学习的年龄和健康感知预测模型，并通过测量特征重要性来确定模型的可解释性。
年龄认知主要由地形特征驱动，特别是50岁以下人群中的眼袋和眼睑下垂。皱纹，尤其是来自面部下部和下脸椭圆形区域的皱纹，在50岁以上人群中更为相关。在50岁以下的子集中，健康外观主要由皮肤瑕疵和整体色素沉着表示，而在50岁以上的子集中，与颜色相关的参数和皮肤水合作用作为健康线索。
不同的皮肤特征作为年龄认知和/或健康认知的线索，并且因年龄子集而异。在设计“看起来更年轻的皮肤”和“看起来更健康的皮肤”产品时，应考虑它们的作用。
