Relative ranking of facial attractiveness
Research output: Contribution to journal › Conference article › Research › peer-review
Automatic evaluation of human facial attractiveness is a challenging problem that has received relatively little attention from the computer vision community. Previous work in this area have posed attractiveness as a classification problem. However, for applications that require fine-grained relationships between objects, learning to rank has been shown to be superior over the direct interpretation of classifier scores as ranks [27]. In this paper, we propose and implement a personalized relative beauty ranking system. Given training data of faces sorted based on a subject's personal taste, we learn how to rank novel faces according to that person's taste. Using a blend of Facial Geometric Relations, HOG, GIST, Lab Color Histograms, and Dense-SIFT + PCA feature types, our system achieves an average accuracy of 63% on pairwise comparisons of novel test faces. We examine the effectiveness of our method through lesion testing and find that the most effective feature types for predicting beauty preferences are HOG, GIST, and Dense-SIFT + PCA features.
Original language | English |
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Journal | Proceedings of IEEE Workshop on Applications of Computer Vision |
Pages (from-to) | 117-124 |
Number of pages | 8 |
ISSN | 2158-3978 |
DOIs | |
Publication status | Published - 2013 |
Externally published | Yes |
Event | 2013 IEEE Workshop on Applications of Computer Vision, WACV 2013 - Clearwater Beach, FL, United States Duration: 15 Jan 2013 → 17 Jan 2013 |
Conference
Conference | 2013 IEEE Workshop on Applications of Computer Vision, WACV 2013 |
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Country | United States |
City | Clearwater Beach, FL |
Period | 15/01/2013 → 17/01/2013 |
ID: 302164685