Behavior and Personality Analysis in a nonsocial context Dataset

Dario Dotti*, Mirela Popa, Stylianos Asteriadis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

3 Citations (Web of Science)


Personality recognition using nonverbal behavioral cues is a challenging task in the Affective Computing field. The majority of existing methods investigate personality assessment in social contexts, such as crowded places or social events, but ignore the role of behaviors as well as personality in nonsocial situations (i.e. during individual activities). In this paper we introduce a novel dataset for behavior understanding and personality recognition in a nonsocial context. Forty-six participants were recorded in an unconstrained indoor space, related to a smart home environment, performing six tasks resembling Activities of Daily Living (ADL). During the experiment, personality scores were collected using self-assessment questionnaires. Furthermore, a temporal framework using a Long-Short Term Memory (LSTM) network is proposed to map nonverbal behavioral features to participants' personality labels. Our experiments showed that nonverbal behaviors are important predictors of personality, confirming theories from the personality psychology field.
Original languageEnglish
Title of host publication2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Number of pages9
ISBN (Print)9781538661000
Publication statusPublished - 2018
EventIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) - UT
Duration: 18 Jun 201822 Jun 2018


ConferenceIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)



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