Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/36325
Title: A new hypothesis on facial beauty perception
Authors: Chen, FM
Xu, Y
Zhang, D 
Keywords: Experimentation
Human Factors
Convex hull
Face beautification
Face perception
Hypothesis
Machine learning
Regression
Issue Date: 2014
Publisher: Association for Computing Machinary
Source: ACM transactions on applied perception, 2014, v. 11, no. 2, 8 How to cite?
Journal: ACM transactions on applied perception 
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.
URI: http://hdl.handle.net/10397/36325
ISSN: 1544-3558 (print)
1544-3965 (online)
DOI: 10.1145/2622655
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

5
Last Week
0
Last month
Citations as of Aug 10, 2017

Page view(s)

47
Last Week
4
Last month
Checked on Aug 14, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.