Towards Approximating Personality Cues Through Simple Daily Activities

Francesco Gibellini, Sebastiaan Higler, Jan Lucas, Migena Luli, Morris Stallmann, Dario Dotti, Stelios Asteriadis

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic


The goal of this work is to investigate the potential of making use of simple activity and motion patterns in a smart environment for approximating personality cues via machine learning techniques. Towards this goal, we present a novel framework for personality recognition, inspired by both computer vision and psychology. Results show a correlation between several behavioral features and personality traits, as well as insights of which type of everyday tasks induce stronger personality display. We experiment with the use of support vector machines, random forests and gaussian process classification achieving promising predictive ability, related to personality traits. The obtained results show consistency to a good degree, opening the path for applications in psychology, game industry, ambient assisted living, and other fields.keywordspersonality recognitionbehavior analysismachine learningpersonality traits.
Original languageEnglish
Title of host publicationAdvanced Concepts for Intelligent Vision Systems. ACIVS 2020
Subtitle of host publicationProceedings International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS 2020)
PublisherSpringer, Cham
ISBN (Electronic)978-3-030-40605-9
ISBN (Print)978-3-030-40604-2
Publication statusPublished - 2020

Publication series

SeriesLecture Notes in Computer Science


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