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 -