Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, Kansas City, Kansas, USA.
Facial Plast Surg Aesthet Med. 2021 Sep;23(5):339-343. doi: 10.1089/fpsam.2020.0328. Epub 2021 Mar 11.
A centralized repository of clinically applicable facial images with unrestricted use would facilitate facial aesthetic research. Using a machine learning neural network, we aim to (1) create a repository of synthetic faces that can be used for facial aesthetic research and (2) analyze synthetic faces according to contemporary aesthetic principles. Synthetic facial images were generated using an open source generative adversarial network. Images were refined and then analyzed using computer vision technology. Not applicable. Synthetic facial images were created for use as a facial aesthetic research data set. One thousand synthetic images were generated, and 60 images underwent analysis. Image attributes, including age, gender, image principle axis, facial emotion, and facial landmark points, were attained. Images demonstrated accordance with contemporary aesthetic principles of horizontal thirds and vertical fifths. Images demonstrated excellent correspondence when compared with real human facial photographs. We have generated realistic synthetic facial images that have potential as a valuable research tool and demonstrate similarity to real human photographs while adhering to contemporary aesthetic principles.
一个集中的、可不受限制使用的临床适用面部图像库将有助于面部美学研究。我们使用机器学习神经网络，旨在（1）创建一个可用于面部美学研究的合成面部图像库，以及（2）根据当代美学原则对面部合成图像进行分析。使用开源生成式对抗网络生成合成面部图像。使用计算机视觉技术对图像进行细化和分析。不适用。合成面部图像被创建为面部美学研究数据集使用。生成了 1000 张合成图像，其中 60 张图像接受了分析。获得了图像属性，包括年龄、性别、图像主轴线、面部表情和面部地标点。图像符合当代美学的三分法和五分法原则。与真实的人类面部照片相比，图像具有极好的一致性。我们已经生成了逼真的合成面部图像，它们有可能成为一种有价值的研究工具，并且在符合当代美学原则的同时，与真实的人类照片具有相似性。
