A New Hypothesis on Facial Beauty Perception

IF 1.9 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Fangmei Chen, Yong Xu, D. Zhang
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引用次数: 14

Abstract

In this article, a new hypothesis on facial beauty perception is proposed: the weighted average of two facial geometric features is more attractive than the inferior one between them. Extensive evidences support the new hypothesis. We collected 390 well-known beautiful face images (e.g., Miss Universe, movie stars, and super models) as well as 409 common face images from multiple sources. Dozens of volunteers rated the face images according to their attractiveness. Statistical regression models are trained on this database. Under the empirical risk principle, the hypothesis is tested on 318,801 pairs of images and receives consistently supportive results. A corollary of the hypothesis is attractive facial geometric features construct a convex set. This corollary derives a convex hull based face beautification method, which guarantees attractiveness and minimizes the before--after difference. Experimental results show its superiority to state-of-the-art geometric based face beautification methods. Moreover, the mainstream hypotheses on facial beauty perception (e.g., the averageness, symmetry, and golden ratio hypotheses) are proved to be compatible with the proposed hypothesis.
面部美感感知的新假说
本文提出了一种新的关于面部美感感知的假设:两个面部几何特征的加权平均值比其中较差的一个更有吸引力。大量的证据支持这个新假设。我们从多个来源收集了390张众所周知的美丽面部图像(如环球小姐、电影明星和超模)和409张普通面部图像。数十名志愿者根据照片的吸引力给它们打分。统计回归模型是在这个数据库上训练的。在经验风险原则下,对318801对图像进行了假设检验,得到了一致的支持结果。该假设的一个推论是吸引面部的几何特征构成一个凸集。这一推论衍生出一种基于凸壳的面部美容方法,既保证了吸引力,又将前后差异降到最低。实验结果表明,该方法优于目前最先进的基于几何的人脸美化方法。此外,研究还证明了面部美感感知的主流假设(如平均、对称和黄金比例假设)与本文提出的假设是相容的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACM Transactions on Applied Perception
ACM Transactions on Applied Perception 工程技术-计算机:软件工程
CiteScore
3.70
自引率
0.00%
发文量
22
审稿时长
12 months
期刊介绍: ACM Transactions on Applied Perception (TAP) aims to strengthen the synergy between computer science and psychology/perception by publishing top quality papers that help to unify research in these fields. The journal publishes inter-disciplinary research of significant and lasting value in any topic area that spans both Computer Science and Perceptual Psychology. All papers must incorporate both perceptual and computer science components.
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