Multimodal recognition of personality traits in social interactions
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
Please, tell me about yourself: automatic personality assessment using short self-presentations
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
Automatic modeling of personality states in small group interactions
MM '11 Proceedings of the 19th ACM international conference on Multimedia
FaceTube: predicting personality from facial expressions of emotion in online conversational video
Proceedings of the 14th ACM international conference on Multimodal interaction
From speech to personality: mapping voice quality and intonation into personality differences
Proceedings of the 20th ACM international conference on Multimedia
Mining large-scale smartphone data for personality studies
Personal and Ubiquitous Computing
Connecting Meeting Behavior with Extraversion—A Systematic Study
IEEE Transactions on Affective Computing
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We present an analysis on personality prediction in small groups based on trait attributes from external observers. We use a rich set of automatically extracted audio-visual nonverbal features, including speaking turn, prosodic, visual activity, and visual focus of attention features. We also investigate whether the thin sliced impressions of external observers generalize to the whole meeting in the personality prediction task. Using ridge regression, we have analyzed both the regression and classification performance of personality prediction. Our experiments show that the extraversion trait can be predicted with high accuracy in a binary classification task and visual activity features give higher accuracies than audio ones. The highest accuracy for the extraversion trait, is 75\%, obtained with a combination of audio-visual features. Openness to experience trait also has a significant accuracy, only when the whole meeting is used as the unit of processing.