Modeling and adjusting in-game difficulty based on facial expression analysis

Research output: Contribution to journalArticleScientificpeer-review

Abstract

In this paper we introduce Facial Expression Analysis (FEA) both as a means of predicting in-game difficulty and as a modeling mechanism, based on which we develop in-game difficulty adjustment algorithms for single player arcade games. Our main contribution is the implementation of an online and unobtrusive game personalisation system. On the basis of FEA, our system is able to adapt the difficulty level of the game to the individual player, without interruptions, during actual gameplay. Specifically, we study (a) how perceived in-game difficulty can be measured through facial expression analysis, and (b) how facial expression data can model player behavior and predict their affective state. Experimental findings reveal that different in-game difficulty settings can be correlated to distinct player emotions (revealed in facial expressions). Furthermore, a model based on facial expression analysis is successfully applied to calculate an appropriate difficulty setting, tailored to the individual player. From these results, we may conclude that efficient game personalisation is achievable through FEA.
Original languageEnglish
JournalEntertainment Computing
Volume31
DOIs
Publication statusPublished - 1 Aug 2019

Keywords

  • Dynamic difficulty adjustment
  • Facial expression analysis
  • Game difficulty adaptation
  • Game personalisation

Cite this

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title = "Modeling and adjusting in-game difficulty based on facial expression analysis",
abstract = "In this paper we introduce Facial Expression Analysis (FEA) both as a means of predicting in-game difficulty and as a modeling mechanism, based on which we develop in-game difficulty adjustment algorithms for single player arcade games. Our main contribution is the implementation of an online and unobtrusive game personalisation system. On the basis of FEA, our system is able to adapt the difficulty level of the game to the individual player, without interruptions, during actual gameplay. Specifically, we study (a) how perceived in-game difficulty can be measured through facial expression analysis, and (b) how facial expression data can model player behavior and predict their affective state. Experimental findings reveal that different in-game difficulty settings can be correlated to distinct player emotions (revealed in facial expressions). Furthermore, a model based on facial expression analysis is successfully applied to calculate an appropriate difficulty setting, tailored to the individual player. From these results, we may conclude that efficient game personalisation is achievable through FEA.",
keywords = "Dynamic difficulty adjustment, Facial expression analysis, Game difficulty adaptation, Game personalisation",
author = "Blom, {Paris Mavromoustakos} and Sander Bakkes and Pieter Spronck",
year = "2019",
month = "8",
day = "1",
doi = "10.1016/j.entcom.2019.100307",
language = "English",
volume = "31",
journal = "Entertainment Computing",
issn = "1875-9521",
publisher = "Elsevier",

}

Modeling and adjusting in-game difficulty based on facial expression analysis. / Blom, Paris Mavromoustakos; Bakkes, Sander; Spronck, Pieter.

In: Entertainment Computing, Vol. 31, 01.08.2019.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

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AU - Blom, Paris Mavromoustakos

AU - Bakkes, Sander

AU - Spronck, Pieter

PY - 2019/8/1

Y1 - 2019/8/1

N2 - In this paper we introduce Facial Expression Analysis (FEA) both as a means of predicting in-game difficulty and as a modeling mechanism, based on which we develop in-game difficulty adjustment algorithms for single player arcade games. Our main contribution is the implementation of an online and unobtrusive game personalisation system. On the basis of FEA, our system is able to adapt the difficulty level of the game to the individual player, without interruptions, during actual gameplay. Specifically, we study (a) how perceived in-game difficulty can be measured through facial expression analysis, and (b) how facial expression data can model player behavior and predict their affective state. Experimental findings reveal that different in-game difficulty settings can be correlated to distinct player emotions (revealed in facial expressions). Furthermore, a model based on facial expression analysis is successfully applied to calculate an appropriate difficulty setting, tailored to the individual player. From these results, we may conclude that efficient game personalisation is achievable through FEA.

AB - In this paper we introduce Facial Expression Analysis (FEA) both as a means of predicting in-game difficulty and as a modeling mechanism, based on which we develop in-game difficulty adjustment algorithms for single player arcade games. Our main contribution is the implementation of an online and unobtrusive game personalisation system. On the basis of FEA, our system is able to adapt the difficulty level of the game to the individual player, without interruptions, during actual gameplay. Specifically, we study (a) how perceived in-game difficulty can be measured through facial expression analysis, and (b) how facial expression data can model player behavior and predict their affective state. Experimental findings reveal that different in-game difficulty settings can be correlated to distinct player emotions (revealed in facial expressions). Furthermore, a model based on facial expression analysis is successfully applied to calculate an appropriate difficulty setting, tailored to the individual player. From these results, we may conclude that efficient game personalisation is achievable through FEA.

KW - Dynamic difficulty adjustment

KW - Facial expression analysis

KW - Game difficulty adaptation

KW - Game personalisation

UR - http://www.mendeley.com/research/modeling-adjusting-ingame-difficulty-based-facial-expression-analysis

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