Assessing facial beauty through proportion analysis by image processing and supervised learning

Authors:
Hatice Gunes;Massimo Piccardi
Affiliations:
Computer Vision Research Group, Faculty of Information Technology, University of Technology, Sydney (UTS), P.O. Box 123, Broadway, NSW 2007, Australia;Computer Vision Research Group, Faculty of Information Technology, University of Technology, Sydney (UTS), P.O. Box 123, Broadway, NSW 2007, Australia
Venue:
International Journal of Human-Computer Studies
Year:
2006

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Abstract

Perception of universal facial beauty has long been debated amongst psychologists and anthropologists. In this paper, we perform experiments to evaluate the extent of universal beauty by surveying a number of diverse human referees to grade a collection of female facial images. Results obtained show that there exists a strong central tendency in the human grades, thus exhibiting agreement on beauty assessment. We then trained an automated classifier using the average human grades as the ground truth and used it to classify an independent test set of facial images. The high accuracy achieved proves that this classifier can be used as a general, automated tool for objective classification of female facial beauty. Potential applications exist in the entertainment industry, cosmetic industry, virtual media, and plastic surgery.