Shinjin Kang1, Soo Kyun Kim2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 219-237, 2022, DOI:10.32604/cmes.2022.018413
- 24 January 2022
Abstract This paper proposes a methodology for using multi-modal data in gameplay to detect outlier behavior. The proposed methodology collects, synchronizes, and quantifies time-series data from webcams, mouses, and keyboards.
Facial expressions are varied on a one-dimensional pleasure axis, and changes in expression in the mouth and eye
areas are detected separately. Furthermore, the keyboard and mouse input frequencies are tracked to determine
the interaction intensity of users. Then, we apply a dynamic time warp algorithm to detect outlier behavior. The
detected outlier behavior graph patterns were the play patterns that the game designer did not More >
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