Multimodal Personality Recognition from Audiovisual Data

Automatic behavior analysis lies at the intersection of different social and technical research domains. The interdisciplinarity of the field, provides researchers with the means to study the manifestations of human constructs, such as personality. A branch of human behavior analyis, the study of personality provides insight into the cognitive and psychological construction of the human being. Research in personality psychology, advances in com- puting power and the development of algorithms, have made it possible to analyze existing data in order to understand how people express their own personality, perceive others’, and what are the variables that influence its manifestation. We are pursuing this line of research because insights into the personality of the user can have an impact on how we interact with technology. Incorporating research on personality recogniton, both from a cognitive as well as an engineering perspective, into computers could facilitate the interactions between humans and machines. Previous attempts on personality recognition have focused on a variety of different corporas (ranging from text to audiovisual data), different scenarios (interviews, meetings), different channels of communication (audio, video, text) and different subsets of personality traits (out of the five ones present in the Big Five Model: Extraversion, Agreeableness, Conscientiousness, Emotional Stability and Creativity). Our work builds on previous research, by considering simple acoustic and visual non-verbal features extracted from multimodal data, but doesn’t fail to bring novelties: we consider previously uninvestigated scenarios, and at the same time, all of the five personality traits and not just a subset.

Grantor dc:publisher

Università degli studi di Trento

Year dc:date

2013

Author dc:creator

• Batrinca, Ligia Maria

Contributors dc:contributor

• Pianesi, Fabio

• Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni

Statement dc:rights

• info:eu-repo/semantics/openAccess
• license:Tutti i diritti riservati (All rights reserved)

Language dc:language

eng

Identifier

http://dx.doi.org/10.15168/11572_369288
10.15168/11572_369288

OAI identifier oai:identifier

oai:iris.unitn.it:11572/369288