Inferring Human Personality Traits in Human-Robot Social Interaction

Zhihao Shen, Armagan Elibol, Nak Young Chong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In this report, a new framework is proposed for inferring the user's personality traits based on their habitual behaviors during face-to-face human-robot interactions, aiming to improve the quality of human-robot interactions. The proposed framework enables the robot to extract the person's visual features such as gaze, head and body motion, and vocal features such as pitch, energy, and Mel-Frequency Cepstral Coefficient (MFCC) during the conversation that is lead by Robot posing a series of questions to each participant. The participants are expected to answer each of the questions with their habitual behaviors. Each participant's personality traits can be assessed with a questionnaire. Then, all data will be used to train the regression or classification model for inferring the user's personality traits.

Original languageEnglish
Title of host publicationHRI 2019 - 14th ACM/IEEE International Conference on Human-Robot Interaction
PublisherIEEE Computer Society
Pages578-579
Number of pages2
ISBN (Electronic)9781538685556
DOIs
Publication statusPublished - 2019 Mar 22
Externally publishedYes
Event14th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2019 - Daegu, Korea, Republic of
Duration: 2019 Mar 112019 Mar 14

Publication series

NameACM/IEEE International Conference on Human-Robot Interaction
Volume2019-March
ISSN (Electronic)2167-2148

Conference

Conference14th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2019
Country/TerritoryKorea, Republic of
CityDaegu
Period19/3/1119/3/14

Keywords

  • classification model
  • human-robot interaction
  • regression model
  • social cue
  • user personality traits

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

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