Adaptive ranking of facial attractiveness
Research output: Contribution to journal › Conference article › Research › peer-review
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.
Original language | English |
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Article number | 6890147 |
Journal | Proceedings - IEEE International Conference on Multimedia and Expo |
Volume | 2014-September |
Issue number | Septmber |
ISSN | 1945-7871 |
DOIs | |
Publication status | Published - 3 Sep 2014 |
Externally published | Yes |
Event | 2014 IEEE International Conference on Multimedia and Expo, ICME 2014 - Chengdu, China Duration: 14 Jul 2014 → 18 Jul 2014 |
Conference
Conference | 2014 IEEE International Conference on Multimedia and Expo, ICME 2014 |
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Country | China |
City | Chengdu |
Period | 14/07/2014 → 18/07/2014 |
Sponsor | Baidu, BOCOM, et al., NSF, NSFC, QIY |
Bibliographical note
Publisher Copyright:
© 2014 IEEE.
- adaptive methods, facial attractiveness, ranking, rating
Research areas
ID: 302043993