Journal of Korea Game Society (한국게임학회 논문지)

Korea Game Society (한국게임학회)
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Expression Analysis System of Game Player based on Multi-modal Interface

멀티 모달 인터페이스 기반 플레이어 얼굴 표정 분석 시스템 개발

  • Received : 2016.03.10
  • Accepted : 2016.04.16
  • Published : 2016.04.20
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Abstract

In this paper, we propose a method for effectively detecting specific behavior. The proposed method detects outlying behavior based on the game players' characteristics. These characteristics are captured non-invasively in a general game environment and add keystroke based on repeated pattern. In this paper, cameras were used to analyze observed data such as facial expressions and player movements. Moreover, multimodal data from the game players was used to analyze high-dimensional game-player data for a detection effect of repeated behaviour pattern. A support vector machine was used to efficiently detect outlying behaviors. We verified the effectiveness of the proposed method using games from several genres. The recall rate of the outlying behavior pre-identified by industry experts was approximately 70%. In addition, Repeated behaviour pattern can be analysed possible. The proposed method can also be used for feedback and quantification about analysis of various interactive content provided in PC environments.

본 논문은 게임을 수행하는 개별 사용자의 특별한 행동 탐지를 효율적으로 수행하는 시스템을 제안한다. 제안하는 시스템은 일반적인 게임 플레이 환경에서 비 침투적 방법을 통해 포착 가능한 사용자 특징과 반복적인 패턴에 기반을 두어 특이 행동 탐지를 한다. 본 논문에서는 표정과 사용자 움직임과 같이 관찰되는 자료를 분석하기 위해 카메라를 사용했다. 게다가 반복 행동 탐지를 위해 게임 사용자로부터 멀티 모달 데이터를 사용하여 고차원의 행동 분석하기 위해 사용했다. 특이 행동 탐지에 효과적인 Support Vector Machine 을 사용했으며, 특이 행동 탐지 수행의 유용성을 평가하여 약 70% 확률로 탐지하는 이상 행동 탐지 재현율을 보였다. 또한 반복 행동 분석이 가능함을 보였다. 제안된 기법을 사용하였을 때 PC 환경에서 제공하는 모든 콘텐츠의 분석에 대한 피드백과 정량화하는데 도움이 될 수 있다.

Keywords

References

  1. Bauckhage, C., Kersting, K., Sifa, R., Thurau, C., Drachen, A., & Canossa, A. How players lose interest in playing a game: An empirical study based on distributions of total playing times. In Computational Intelligence and Games (CIG), September, pp.139-146, 2012.
  2. Isbister, K., & Schaffer, N. Game usability: Advancing the player experience. CRC Press. 2008
  3. Lang, P. J. The emotion probe: Studies of motivation and attention.American psychologist, 50(5), pp.372, 1995. https://doi.org/10.1037/0003-066X.50.5.372
  4. Sanchez, J. L. G., Vela, F. L. G., Simarro, F. M., & Padilla-Zea, N. . Playability: analysing user experience in video games. Behaviour & Information Technology, 31(10), pp.1033-1054, 2012 https://doi.org/10.1080/0144929X.2012.710648
  5. Po-Ming Lee, Wei-Hsuan Tsui, and Tzu-Chien Hsiao, A Low-C Scalable Solution for Monitoring Affective State of Students in E-learning Environment Using Mouse and Keystroke Data. Springer-Verlag Berlin Heidelberg. pp. 679-680. 2012.
  6. K. H. Kim, S. W. Bang, S. R. Kim, Emotion recognition system using short-term monitoring of physiological signals. Med. Biol. Eng. Comput. 2004, pp.419-427. 2004.
  7. Ji-Hun Lee, A Study on Factors to Affect Reuse Intention and Conversion Intention by Evaluation after Game Use - with Two FPS Games as a Main Consideration - , Journal of Korea Game Society, Vol. 9, No. 6, 2009.
  8. Bo-Mee Yoo, Seoung-Ho Ryu, A Case Study of the Mod: a Developmental Perspective in Game Development Process - Focusing on Customer Involvement - ,Journal of Korea Game Society, Vol. 9, No. 1, 2009.
  9. Del Viva MM, Punzi G, Benedetti D Information and Perception of Meaningful Patterns. PLoS ONE 8(7): e69154. 2013. https://doi.org/10.1371/journal.pone.0069154
  10. Pagulayan, R. J., Keeker, K., Wixon, D., Romero, R. L., & Fuller, T. User-centered design in games. The human-computer interaction handbook: fundamentals, evolving technologies and emerging applications, pp.883-906, 2003.
  11. DAOJINFAN, YU SUN, Analysis of Innovation Behavior Impact Based on Repeated Game. IEEE. Tianjin University of Technology. 2013.
  12. El-Nasr, M. S., Drachen, A., & Canossa, A. Game analytics: Maximizing the value of player data, Springer, 2013.
  13. Canossa, A., Drachen, A., & Sorensen, J. R. M. (2011, June). Arrrgghh!!!: blending quantitative and qualitative methods to detect player frustration. In Proceedings of the 6th International Conference on Foundations of Digital Games. ACM, June, pp. 61-68, 2011.
  14. Drachen, A., Thurau, C., Sifa, R., & Bauckhage, C. A comparison of methods for player clustering via behavioral telemetry, 2013.
  15. Kennerly, D. Better game design through data mining. Gamasutra, August, pp.15, 2003.
  16. Zoeller, G. Game development telemetry. In Proceedings of the Game Developers Conference, 2010.
  17. Iftikhar Ahmed Khan, Osman Khalid, Waqas Jadoon, Rafi Us Shan, Abdul Nasir Khan, Predicting Programmers' Personality via Interaction Behaviour with Keyboard & Mouse. PeerJ COMSATS Institute of Information Technology. 2015.
  18. Caitlin Mills, Nigel Bosch, Art Graesser ,Sidney D'Mello, To Quit or Not to Quit: Predicting Future Behavioral Disengagement from Reading Patterns. Springer International Publishing Switzerland 2014. S. Trausan-Matu et al. (Eds.), LNCS 8474, pp. 19-28, 2014.
  19. Lisa M.Vizer, LinaZhou, AndrewSears, Automated stress detection using keystroke and linguistic features: An exploratory study. Int. J. Human-Computer Studies 67. pp. 870-886. 2009. https://doi.org/10.1016/j.ijhcs.2009.07.005
  20. Po-Ming Lee, Wei-Hsuan Tsui, Tzu-Chien Hsiao, The Influence of Emotion on Keyboard Typing: An Experimental Study Using Auditory Stimuli. PLOS ONE l DOI:10.1371/journal.pone.0129056. 2015.
  21. YoungBin Kim, Shin Jin Kang, SangHyeok Lee, Jang Young Jung, Hyeong Ryeol Kam, Jung Lee, YoungSun Kim, Joonsoo Lee, Chang Hun Kim, Efficiently detecting outlying behavior in video-game players. PeerJ reviewing PDF l (2015:06:5577:2:0:NEW 23 Nov. 2015.
  22. Cootes, T. F., Edwards, G. J., & Taylor, C. J. Active appearance models. IEEE Transactions on pattern analysis and machine intelligence,23(6), pp.681-685, 2001. https://doi.org/10.1109/34.927467
  23. Bradski, G. The opencv library. Doctor Dobbs Journal, 25(11), pp.120-126, 2000.
  24. Karthigayan, M., Rizon, M., Nagarajan, R., & Yaacob, S. Genetic algorithm and neural network for face emotion recognition. Affective Computing, pp.57-68, 2008.
  25. http://www.emotionlab.se/resources/kdef
  26. Chang, C. C., & Lin, C. J. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST),2(3), pp.27, 2011.