What Your Face Vlogs About: Expressions of Emotion and Big-Five Traits Impressions in YouTube

Social video sites where people share their opinions and feelings are increasing in popularity. The face is known to reveal important aspects of human psychological traits, so the understanding of how facial expressions relate to personal constructs is a relevant problem in social media. We present...

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Bibliographic Details
Published in:IEEE transactions on affective computing 2015-04, Vol.6 (2), p.193-205
Main Authors: Teijeiro-Mosquera, Lucıa, Biel, Joan-Isaac, Alba-Castro, Jose Luis, Gatica-Perez, Daniel
Format: Article
Language:English
Subjects:
Correlation
Face
Face processing
Face recognition
Facial Expressions
Facial features
Feature extraction
Hidden Markov models
Personality Prediction
Social networks
vlogs
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Summary:Social video sites where people share their opinions and feelings are increasing in popularity. The face is known to reveal important aspects of human psychological traits, so the understanding of how facial expressions relate to personal constructs is a relevant problem in social media. We present a study of the connections between automatically extracted facial expressions of emotion and impressions of Big-Five personality traits in YouTube vlogs (i.e., video blogs). We use the Computer Expression Recognition Toolbox (CERT) system to characterize users of conversational vlogs. From CERT temporal signals corresponding to instantaneously recognized facial expression categories, we propose and derive four sets of behavioral cues that characterize face statistics and dynamics in a compact way. The cue sets are first used in a correlation analysis to assess the relevance of each facial expression of emotion with respect to Big-Five impressions obtained from crowd-observers watching vlogs, and also as features for automatic personality impression prediction. Using a dataset of 281 vloggers, the study shows that while multiple facial expression cues have significant correlation with several of the Big-Five traits, they are only able to significantly predict Extraversion impressions with moderate values of R 2 .
ISSN:1949-3045
1949-3045
DOI:10.1109/TAFFC.2014.2370044