Face in the Game: Using Facial Action Units to Track Expertise in Competitive Video Game Play

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

    Abstract

    In this study, we extracted facial action units (AUs)
    data during a Hearthstone tournament to investigate behavioural
    differences between expert, intermediate, and novice players. Our
    aim was to obtain insights into the nature of expertise and how it
    may be tracked using non-invasive methods such as AUs. These
    insights may shed light on the endogenous responses in the player
    and at the same time may provide information to the opponents
    during a competition. Our results show that player expertise may
    be characterised by specific patterns in facial expressions. More
    specifically, AU17, AU25, and AU26 intensity responses during
    gameplay may vary according to players' expertise. Such results
    were obtained by training a random forest classifier to test
    whether we can use these three AUs alone to accurately detect
    players' expertise. The classifier reached 0.75 accuracy on 5-fold
    cross-validation, after balancing the class weights, and 0.85 after
    having applied the SMOTE function. These results suggest that
    AUs can be effectively used to discriminate different levels of
    expertise in competitive video game players.
    Original languageEnglish
    Title of host publication2022 IEEE Conference On Games (CoG)
    PublisherIEEE
    Pages112-118
    ISBN (Electronic)978-1-6654-5989-1
    ISBN (Print)978-1-6654-5990-7
    DOIs
    Publication statusPublished - 30 Apr 2022

    Keywords

    • Video Games
    • Player Modelling
    • Expertise
    • Machine Learning

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