Game Outlier Behavior Detection System Based on Dynamic Time Warp Algorithm  

作　　者：Shinjin Kang Soo Kyun Kim 

机构地区：[1]School of Games,Hongik University,Sejong,30016,Korea [2]Department of Computer Engineering,Jeju National University,Jeju,63243,Korea

出　　处：《Computer Modeling in Engineering & Sciences》2022年第4期219-237,共19页工程与科学中的计算机建模（英文）

基　　金：This research was supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1I1A3058103).

摘　　要：This paper proposes a methodology for using multi-modal data in gameplay to detect outlier behavior.The proposedmethodology collects,synchronizes,and quantifies time-series data fromwebcams,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 intend or play patterns that differed greatly from those of other users.These outlier patterns can provide game designers with feedback on the actual play experiences of users of the game.Our results can be applied to the game industry as game user experience analysis,enabling a quantitative evaluation of the excitement of a game.

关 键 词：Facial expression recognition WEBCAM behavior analysis affective computing 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]