Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31974
Title: A benchmark for geometric facial beauty study
Authors: Chen, F
Zhang, D 
Issue Date: 2010
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2010, v. 6165 LNCS, p. 21-32 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: This paper presents statistical analyses for facial beauty study. A large-scale database was built, containing 23412 frontal face images, 875 of them are marked as beautiful. We focus on the geometric feature defined by a set of landmarks on faces. A normalization approach is proposed to filter out the non-shape variations - translation, rotation, and scale. The normalized features are then mapped to its tangent space, in which we conduct statistical analyses: Hotelling's T2 test is applied for testing whether female and male mean faces have significant difference; Principal Component Analysis (PCA) is applied to summarize the main modes of shape variation and do dimension reduction; A criterion based on the Kullback-Leibler (KL) divergence is proposed to evaluate different hypotheses and models. The KL divergence measures the distribution difference between the beautiful group and the whole population. The results show that male and female faces come from different Gaussian distributions, but the two distributions overlap each other severely. By measuring the KL divergence, it shows that multivariate Gaussian model embodies much more beauty related information than the averageness hypothesis and the symmetry hypothesis. We hope the large-scale database and the proposed evaluation methods can serve as a benchmark for further studies.
Description: 2nd International Conference on Medical Biometrics, ICMB 2010, Hong Kong, 28-30 June 2010
URI: http://hdl.handle.net/10397/31974
ISBN: 3642139221
9783642139222
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-642-13923-9_3
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