Affective computing
Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
The human-computer interaction handbook
What makes computer games fun? (abstract only)
CHI '81 Proceedings of the Joint Conference on Easier and More Productive Use of Computer Systems. (Part - II): Human Interface and the User Interface - Volume 1981
Physiological indicators for the evaluation of co-located collaborative play
CSCW '04 Proceedings of the 2004 ACM conference on Computer supported cooperative work
A Theory of Fun for Game Design
A Theory of Fun for Game Design
Robust feature detection for facial expression recognition
Journal on Image and Video Processing
Affective Loop Experiences --- What Are They?
PERSUASIVE '08 Proceedings of the 3rd international conference on Persuasive Technology
Multimedia Tools and Applications
Explorations in engagement for humans and robots
Artificial Intelligence
Modeling player experience in super mario bros
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Preference learning for cognitive modeling: a case study on entertainment preferences
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Closing the loop: from affect recognition to empathic interaction
Proceedings of the 3rd international workshop on Affective interaction in natural environments
In-Game: From Immersion to Incorporation
In-Game: From Immersion to Incorporation
Experience-Driven Procedural Content Generation
IEEE Transactions on Affective Computing
Determining driver visual attention with one camera
IEEE Transactions on Intelligent Transportation Systems
Towards player-driven procedural content generation
Proceedings of the 9th conference on Computing Frontiers
Visual Focus of Attention in Non-calibrated Environments using Gaze Estimation
International Journal of Computer Vision
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Recognizing players' affective state while playing video games has been the focus of many recent research studies. In this paper we describe the process that has been followed to build a corpus based on game events and recorded video sessions from human players while playing Super Mario Bros. We present different types of information that have been extracted from game context, player preferences and perception of the game, as well as user features, automatically extracted from video recordings. We run a number of initial experiments to analyse players' behavior while playing video games as a case study of the possible use of the corpus.