Facial symmetry is a key component in quantifying the perception of beauty. In this paper, we propose a set of facial features computed from facial landmarks which can be extracted at a low computational cost. We quantitatively evaluated the proposed features for predicting perceived attractiveness from human portraits on four benchmark datasets (SCUT-FBP, SCUT-FBP5500, FACES and Chicago Face Database). Experimental results showed that the performance of the proposed features is comparable to those extracted from a set with much denser facial landmarks. The computation of facial features was also implemented as an augmented reality (AR) app developed on Android OS. The app overlays four types of measurements and guidelines over a live video stream, while the facial measurements are computed from the tracked facial landmarks at run time. The developed app can be used to assist plastic surgeons in assessing facial symmetry when planning reconstructive facial surgeries.

面部对称是量化对美感的关键要素。在本文中，我们提出了一组从面部地标计算出的面部特征，这些特征可以以较低的计算成本进行提取。我们在四个基准数据集（SCUT-FBP，SCUT-FBP5500，FACES和Chicago Face数据库）上定量评估了拟议的功能，以预测人像的吸引力。实验结果表明，所提出特征的性能可与从具有更密集面部轮廓的集合中提取的特征相媲美。面部特征的计算也被实现为在Android OS上开发的增强现实（AR）应用程序。该应用程序在实时视频流上覆盖了四种类型的测量和指导，而面部测量则是在运行时根据跟踪的面部标志来计算的。