Obwegeser Dorothea, Timofte Radu, Mayer Christoph, Bornstein Michael M, Schätzle Marc A, Patcas Raphael
Clinic of Orthodontics and Pediatric Dentistry, Center of Dental Medicine, University of Zurich, Zurich, Switzerland.
Computer Vision Laboratory, Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland.
Orthod Craniofac Res. 2024 Dec;27 Suppl 2(Suppl 2):25-32. doi: 10.1111/ocr.12820. Epub 2024 Jun 2.
In many medical disciplines, facial attractiveness is part of the diagnosis, yet its scoring might be confounded by facial expressions. The intent was to apply deep convolutional neural networks (CNN) to identify how facial expressions affect facial attractiveness and to explore whether a dedicated training of the CNN is able to reduce the bias of facial expressions.
Frontal facial images (n = 840) of 40 female participants (mean age 24.5 years) were taken adapting a neutral facial expression and the six universal facial expressions. Facial attractiveness was computed by means of a face detector, deep convolutional neural networks, standard support vector regression for facial beauty, visual regularized collaborative filtering and a regression technique for handling visual queries without rating history. CNN was first trained on random facial photographs from a dating website and then further trained on the Chicago Face Database (CFD) to increase its suitability to medical conditions. Both algorithms scored every image for attractiveness.
Facial expressions affect facial attractiveness scores significantly. Scores from CNN additionally trained on CFD had less variability between the expressions (range 54.3-60.9 compared to range: 32.6-49.5) and less variance within the scores (P ≤ .05), but also caused a shift in the ranking of the expressions' facial attractiveness.
Facial expressions confound attractiveness scores. Training on norming images generated scores less susceptible to distortion, but more difficult to interpret. Scoring facial attractiveness based on CNN seems promising, but AI solutions must be developed on CNN trained to recognize facial expressions as distractors.
在许多医学学科中，面部吸引力是诊断的一部分，但其评分可能会受到面部表情的干扰。本研究旨在应用深度卷积神经网络（CNN）来识别面部表情如何影响面部吸引力，并探讨对CNN进行专门训练是否能够减少面部表情的偏差。
对40名女性参与者（平均年龄24.5岁）的正面面部图像（n = 840）进行采集，使其呈现中性面部表情以及六种通用面部表情。通过面部检测器、深度卷积神经网络、用于面部美的标准支持向量回归、视觉正则化协同过滤以及一种用于处理无评分历史视觉查询的回归技术来计算面部吸引力。CNN首先在一个约会网站的随机面部照片上进行训练，然后在芝加哥面部数据库（CFD）上进一步训练，以提高其对医学状况的适用性。两种算法都对每张图像的吸引力进行评分。
面部表情对面部吸引力得分有显著影响。在CFD上进一步训练的CNN得分在不同表情之间的变异性较小（范围为54.3 - 60.9，而之前为32.6 - 49.5），得分内部的方差也较小（P≤0.05），但也导致了表情面部吸引力排名的变化。
面部表情会混淆吸引力得分。在标准化图像上进行训练产生的得分更不易受扭曲影响，但更难解释。基于CNN对面部吸引力进行评分似乎很有前景，但必须在经过训练以将面部表情识别为干扰因素的CNN上开发人工智能解决方案。
