Automatic point-based facial trait judgments evaluation

Mario Rojas Q., David Masip, Alexander Todorov, Jordi Vitrià

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Humans constantly evaluate the personalities of other people using their faces. Facial trait judgments have been studied in the psychological field, and have been determined to influence important social outcomes of our lives, such as elections outcomes and social relationships. Recent work on textual descriptions of faces has shown that trait judgments are highly correlated. Further, behavioral studies suggest that two orthogonal dimensions, valence and dominance, can describe the basis of the human judgments from faces. In this paper, we used a corpus of behavioral data of judgments on different trait dimensions to automatically learn a trait predictor from facial pixel images. We study whether trait evaluations performed by humans can be learned using machine learning classifiers, and used later in automatic evaluations of new facial images. The experiments performed using local point-based descriptors show promising results in the evaluation of the main traits.

Original languageEnglish (US)
Title of host publication2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010
Pages2715-2720
Number of pages6
DOIs
StatePublished - 2010
Event2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010 - San Francisco, CA, United States
Duration: Jun 13 2010Jun 18 2010

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Other

Other2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010
Country/TerritoryUnited States
CitySan Francisco, CA
Period6/13/106/18/10

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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