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International Journal of Computer Games Technology
Volume 2018 (2018), Article ID 8734540, 14 pages
Research Article

Automated Analysis of Facial Cues from Videos as a Potential Method for Differentiating Stress and Boredom of Players in Games

1University of Skövde, Skövde, Sweden
2Federal University of Fronteira Sul, Chapecó, SC, Brazil

Correspondence should be addressed to Fernando Bevilacqua

Received 5 December 2017; Revised 22 January 2018; Accepted 30 January 2018; Published 8 March 2018

Academic Editor: Michael J. Katchabaw

Copyright © 2018 Fernando Bevilacqua et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Facial analysis is a promising approach to detect emotions of players unobtrusively; however approaches are commonly evaluated in contexts not related to games or facial cues are derived from models not designed for analysis of emotions during interactions with games. We present a method for automated analysis of facial cues from videos as a potential tool for detecting stress and boredom of players behaving naturally while playing games. Computer vision is used to automatically and unobtrusively extract 7 facial features aimed at detecting the activity of a set of facial muscles. Features are mainly based on the Euclidean distance of facial landmarks and do not rely on predefined facial expressions, training of a model, or the use of facial standards. An empirical evaluation was conducted on video recordings of an experiment involving games as emotion elicitation sources. Results show statistically significant differences in the values of facial features during boring and stressful periods of gameplay for 5 of the 7 features. We believe our approach is more user-tailored, convenient, and better suited for contexts involving games.