Adaptive ranking of facial attractiveness
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Adaptive ranking of facial attractiveness. / Cao, Chong; Kwak, Iljung Sam; Belongie, Serge; Kriegman, David; Ai, Haizhou.
In: Proceedings - IEEE International Conference on Multimedia and Expo, Vol. 2014-September, No. Septmber, 6890147, 03.09.2014.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Adaptive ranking of facial attractiveness
AU - Cao, Chong
AU - Kwak, Iljung Sam
AU - Belongie, Serge
AU - Kriegman, David
AU - Ai, Haizhou
N1 - Publisher Copyright: © 2014 IEEE.
PY - 2014/9/3
Y1 - 2014/9/3
N2 - As humans, we love to rank things. Top ten lists exist for everything from movie stars to scary animals. Ambiguities (i.e., ties) naturally occur in the process of ranking when people feel they cannot distinguish two items. Human reported rankings derived from star ratings abound on recommendation websites such as Yelp and Netflix. However, those websites differ in star precision which points to the need for ranking systems that adapt to an individual user's preference sensitivity. In this work we propose an adaptive system that allows for ties when collecting ranking data. Using this system, we propose a framework for obtaining computer-generated rankings. We test our system and a computer-generated ranking method on the problem of evaluating human attractiveness. Extensive experimental evaluations and analysis demonstrate the effectiveness and efficiency of our work.
AB - As humans, we love to rank things. Top ten lists exist for everything from movie stars to scary animals. Ambiguities (i.e., ties) naturally occur in the process of ranking when people feel they cannot distinguish two items. Human reported rankings derived from star ratings abound on recommendation websites such as Yelp and Netflix. However, those websites differ in star precision which points to the need for ranking systems that adapt to an individual user's preference sensitivity. In this work we propose an adaptive system that allows for ties when collecting ranking data. Using this system, we propose a framework for obtaining computer-generated rankings. We test our system and a computer-generated ranking method on the problem of evaluating human attractiveness. Extensive experimental evaluations and analysis demonstrate the effectiveness and efficiency of our work.
KW - adaptive methods
KW - facial attractiveness
KW - ranking
KW - rating
UR - http://www.scopus.com/inward/record.url?scp=84937509503&partnerID=8YFLogxK
U2 - 10.1109/ICME.2014.6890147
DO - 10.1109/ICME.2014.6890147
M3 - Conference article
AN - SCOPUS:84937509503
VL - 2014-September
JO - Proceedings - IEEE International Conference on Multimedia and Expo
JF - Proceedings - IEEE International Conference on Multimedia and Expo
SN - 1945-7871
IS - Septmber
M1 - 6890147
T2 - 2014 IEEE International Conference on Multimedia and Expo, ICME 2014
Y2 - 14 July 2014 through 18 July 2014
ER -
ID: 302043993