Adaptive ranking of facial attractiveness

Research output: Contribution to journal › Conference article › Research › peer-review

Standard

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

Harvard

Cao, C, Kwak, IS, Belongie, S, Kriegman, D & Ai, H 2014, 'Adaptive ranking of facial attractiveness', Proceedings - IEEE International Conference on Multimedia and Expo, vol. 2014-September, no. Septmber, 6890147. https://doi.org/10.1109/ICME.2014.6890147

APA

Cao, C., Kwak, I. S., Belongie, S., Kriegman, D., & Ai, H. (2014). Adaptive ranking of facial attractiveness. Proceedings - IEEE International Conference on Multimedia and Expo, 2014-September(Septmber), [6890147]. https://doi.org/10.1109/ICME.2014.6890147

Vancouver

Cao C, Kwak IS, Belongie S, Kriegman D, Ai H. Adaptive ranking of facial attractiveness. Proceedings - IEEE International Conference on Multimedia and Expo. 2014 Sep 3;2014-September(Septmber). 6890147. https://doi.org/10.1109/ICME.2014.6890147

Author

Cao, Chong ; Kwak, Iljung Sam ; Belongie, Serge ; Kriegman, David ; Ai, Haizhou. / Adaptive ranking of facial attractiveness. In: Proceedings - IEEE International Conference on Multimedia and Expo. 2014 ; Vol. 2014-September, No. Septmber.

Bibtex

@inproceedings{a51035d9d00e45e68d2eb114b770b485,
title = "Adaptive ranking of facial attractiveness",
abstract = "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.",
keywords = "adaptive methods, facial attractiveness, ranking, rating",
author = "Chong Cao and Kwak, {Iljung Sam} and Serge Belongie and David Kriegman and Haizhou Ai",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE International Conference on Multimedia and Expo, ICME 2014 ; Conference date: 14-07-2014 Through 18-07-2014",
year = "2014",
month = sep,
day = "3",
doi = "10.1109/ICME.2014.6890147",
language = "English",
volume = "2014-September",
journal = "Proceedings - IEEE International Conference on Multimedia and Expo",
issn = "1945-7871",
number = "Septmber",

}

RIS

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