Relative ranking of facial attractiveness
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Relative ranking of facial attractiveness. / Altwaijry, Hani; Belongie, Serge.
In: Proceedings of IEEE Workshop on Applications of Computer Vision, 2013, p. 117-124.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Relative ranking of facial attractiveness
AU - Altwaijry, Hani
AU - Belongie, Serge
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84875615352&partnerID=8YFLogxK
U2 - 10.1109/WACV.2013.6475008
DO - 10.1109/WACV.2013.6475008
M3 - Conference article
AN - SCOPUS:84875615352
SP - 117
EP - 124
JO - Proceedings of IEEE Workshop on Applications of Computer Vision
JF - Proceedings of IEEE Workshop on Applications of Computer Vision
SN - 2158-3978
T2 - 2013 IEEE Workshop on Applications of Computer Vision, WACV 2013
Y2 - 15 January 2013 through 17 January 2013
ER -
ID: 302164685