Jiang Ruowei, Kezele Irina, Levinshtein Alex, Flament Frederic, Zhang Jingyi, Elmoznino Eric, Ma Junwei, Ma He, Coquide Jerome, Arcin Vincent, Omoyuri Esohe, Aarabi Parham
ModiFace - A L'Oréal Group Company, Toronto, Canada.
L'Oréal Research and Innovation, Clichy, France.
Int J Cosmet Sci. 2019 Feb;41(1):67-78. doi: 10.1111/ics.12512.
To develop an automatic system that grades the severity of facial signs through 'selfies' pictures taken by women of different ages and ethnics.
1140 women from three ethnics (African-American, Asian, Caucasian), of different ages (18-80 years old), took 'selfies' by high resolution smartphones cameras under different conditions of lighting or facial expressions. A dedicated software, was developed, based on a Convolutional Neural Network (CNN) that integrates training data from referential Skin Aging Atlases. The latter allows to an immediate quantification of the severity of nine facial signs according to the ethnicity declared by the subject. These automatic grading were confronted to those assessed by 12 trained experts and dermatologists either on 'selfies' pictures or in live conditions on a smaller cohort of women.
The system appears weakly influenced by lighting conditions or facial expressions (coefficients of variations ranging 10-13% for most signs) and leads to global agreements with experts' assessments, even showing a better reproducibility on some facial signs.
This automatic scoring system, still in development, seems offering a new quantitative approach in the quantified description of facial signs, independent from human vision, in many applications, being individual, cosmetic oriented or dermatological with regard to the follow-up of medical anti-ageing corrective strategies.
开发一种自动系统，通过不同年龄和种族女性拍摄的“自拍”照片对面部体征的严重程度进行分级。
1140名来自三个种族（非裔美国人、亚洲人、白种人）、不同年龄（18 - 80岁）的女性，在不同光照条件或面部表情下，使用高分辨率智能手机摄像头拍摄“自拍”。基于卷积神经网络（CNN）开发了一款专用软件，该网络整合了来自参考皮肤老化图谱的训练数据。后者能够根据受试者申报的种族，立即对九种面部体征的严重程度进行量化。将这些自动分级结果与12名训练有素的专家和皮肤科医生在“自拍”照片上或在一小群女性的实际情况下评估的结果进行对比。
该系统似乎受光照条件或面部表情的影响较小（大多数体征的变异系数在10 - 13%之间），并与专家评估结果达成总体一致，甚至在某些面部体征上显示出更好的可重复性。
这种仍在开发中的自动评分系统，似乎在许多应用中，无论是针对个体、美容导向还是皮肤科领域中医疗抗老化矫正策略的随访，都能在不依赖人类视觉的情况下，对面部体征的量化描述提供一种新的定量方法。
