Computational facial attractiveness prediction by aesthetics-aware features

Authors:
Yadong Mu
Affiliations:
Department of Electrical Engineering, Columbia University, NY 10027, USA
Venue:
Neurocomputing
Year:
2013

Citing 5
Cited 0

Active Appearance Models

ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Facial Attractiveness: Beauty and the Machine

Neural Computation
Data-driven enhancement of facial attractiveness

ACM SIGGRAPH 2008 papers
Computation of a face attractiveness index based on neoclassical canons, symmetry, and golden ratios

Pattern Recognition
Predicting facial beauty without landmarks

ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI

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Abstract

In this paper we address a novel topic, i.e., facial attractiveness prediction, which has both theoretic interest and numerous potential applications such as online dating and face beautification. Our focus is a computational model for prediction purpose. To this end, we briefly review existing algorithms and accompanying benchmarks. To overcome the drawbacks of prior study, we introduce a new dataset with high-quality landmark annotations and multiple facial attractiveness ratings. Several features (both appearance or geometric based) and baseline algorithms are validated on the data. Especially, an aesthetics sensitivity test is performed by utilizing the statistical Lasso. Other concerns such as prediction accuracies and personalized regression are also discussed in the evaluation.