Inferring player experiences using facial expressions analysis

Tan, CT; Bakkes, S; Pisan, Y

Inferring player experiences using facial expressions analysis

Tan, CT Bakkes, S Pisan, Y

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Publication Type:: Conference Proceeding
Citation:: ACM International Conference Proceeding Series, 2014, 02-03-December-2014
Issue Date:: 2014-12-02

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Field	Value	Language
dc.contributor.author	Tan, CT	en_US
dc.contributor.author	Bakkes, S	en_US
dc.contributor.author	Pisan, Y https://orcid.org/0000-0002-9043-5803	en_US
dc.date.issued	2014-12-02	en_US
dc.identifier.citation	ACM International Conference Proceeding Series, 2014, 02-03-December-2014	en_US
dc.identifier.isbn	9781450327909	en_US
dc.identifier.uri	http://hdl.handle.net/10453/42000
dc.description.abstract	© 2014 ACM. Understanding player experiences is central to game design. Video captures of players is a common practice for obtaining rich reviewable data for analysing these experiences. However, not enough has been done in investigating ways of preprocessing the video for a more efficient analysis process. This paper consolidates and extends prior work on validating the feasibility of using automated facial expressions analysis as a natural quantitative method for evaluating player experiences. A study was performed on participants playing a first-person puzzle shooter game (Portal 2) and a social drawing trivia game (Draw My Thing), and results were shown to exhibit rich details for inferring player experiences from facial expressions. Significant correlations were also observed between facial expression intensities and self reports from the Game Experience Questionnaire. In particular, the challenge dimension consistently showed positive correlations with anger and joy. This paper eventually presents a case for increasing the application of computer vision in video analyses of gameplay.	en_US
dc.relation.ispartof	ACM International Conference Proceeding Series	en_US
dc.relation.isbasedon	10.1145/2677758.2677765	en_US
dc.title	Inferring player experiences using facial expressions analysis	en_US
dc.type	Conference Proceeding
utslib.citation.volume	02-03-December-2014	en_US
utslib.for	080111 Virtual Reality and Related Simulation	en_US
utslib.for	080602 Computer-Human Interaction	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - HCTD - Human Centred Technology Design
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	02-03-December-2014	en_US

Abstract:

© 2014 ACM. Understanding player experiences is central to game design. Video captures of players is a common practice for obtaining rich reviewable data for analysing these experiences. However, not enough has been done in investigating ways of preprocessing the video for a more efficient analysis process. This paper consolidates and extends prior work on validating the feasibility of using automated facial expressions analysis as a natural quantitative method for evaluating player experiences. A study was performed on participants playing a first-person puzzle shooter game (Portal 2) and a social drawing trivia game (Draw My Thing), and results were shown to exhibit rich details for inferring player experiences from facial expressions. Significant correlations were also observed between facial expression intensities and self reports from the Game Experience Questionnaire. In particular, the challenge dimension consistently showed positive correlations with anger and joy. This paper eventually presents a case for increasing the application of computer vision in video analyses of gameplay.

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http://hdl.handle.net/10453/42000