Automatic prediction of perceived traits using visual cues under varied situational context

Jyoti Joshi, Hatice Gunes, Roland GOECKE

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

27 Citations (Scopus)
2 Downloads (Pure)


Automatic assessment of human personality traits is a non-trivial problem, especially when perception is marked over a fairly short duration of time. In this study, thin slices of behavioral data are analyzed. Perceived physical and behavioral traits are assessed by external observers (raters). Along with the big-five personality trait model, four new traits are introduced and assessed in this work. The relationship between various traits is investigated to obtain a better understanding of observer perception and assessment. Perception change is also considered when participants interact with several virtual characters each with a distinct emotional style. Encapsulating these observations and analysis, an automated system is proposed by firstly computing low level visual features. Using these features a separate model is trained for each trait and performance is evaluated. Further, a weighted model based on rater credibility is proposed to address observer biases. Experimental results indicate that a weighted model show major improvement for automatic prediction of perceived physical and behavioral traits.
Original languageEnglish
Title of host publication2014 22nd International Conference on Pattern Recognition
Editors Borga
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781479952083
ISBN (Print)9781479952083
Publication statusPublished - 2014
Event22nd International Conference on Pattern Recognition - Stockholm, Stockholm, Sweden
Duration: 24 Aug 201428 Aug 2014

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference22nd International Conference on Pattern Recognition

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