Behavior and Personality Analysis in a Nonsocial Context Dataset

Abstract

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.