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.
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 language | English |
|---|---|
| Title of host publication | 2022 IEEE Conference On Games (CoG) |
| Publisher | IEEE |
| Pages | 112-118 |
| ISBN (Electronic) | 978-1-6654-5989-1 |
| ISBN (Print) | 978-1-6654-5990-7 |
| DOIs | |
| Publication status | Published - 30 Apr 2022 |
Keywords
- Video Games
- Player Modelling
- Expertise
- Machine Learning