Geeks and guests: Estimating player's level of experience from board game behaviors

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

Abstract

Board games have become promising tools for observing and studying social behaviors in multi-person settings. While traditional methods such as self-report questionnaires are used to analyze game-induced behaviors, there is a growing need to automate such analyses. In this paper, we focus on estimating the levels of board game experience by analyzing a player's confidence and anxiety from visual cues. We use a board game setting to induce relevant interactions, and investigate facial expressions during critical game events. For our analysis, we annotated the critical game events in a multiplayer cooperative board game, using the publicly available MUMBAI board game corpus. Using off-the-shelf tools, we encoded facial behavior in dyadic interactions and built classifiers to predict each player's level of experience. Our results show that considering the experience level of both parties involved in the interaction simultaneously improves the prediction results.

Original languageEnglish
Title of host publicationIEEE Winter Applications and Computer Vision Workshops (WACVW)
PublisherIEEE
Pages22-30
Number of pages9
ISBN (Electronic)9781665419673
DOIs
Publication statusPublished - 21 Apr 2021
Event2021 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2021 - Virtual, Waikola, United States
Duration: 5 Jan 20219 Jan 2021

Conference

Conference2021 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2021
Country/TerritoryUnited States
CityVirtual, Waikola
Period5/01/219/01/21

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