title: Efficiently detecting outlying behavior in video-game players.
abstract: In this paper, we propose a method for automatically detecting the times during which game players exhibit specific behavior, such as when players commonly show excitement, concentration, immersion, and surprise. The proposed method detects such outlying behavior based on the game players' characteristics. These characteristics are captured non-invasively in a general game environment. In this paper, cameras were used to analyze observed data such as facial expressions and player movements. Moreover, multimodal data from the game players (i.e., data regarding adjustments to the volume and the use of the keyboard and mouse) was used to analyze high-dimensional game-player data. 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%. The proposed method can also be used for feedback analysis of various interactive content provided in PC environments.
authors.affiliation: Interdisciplinary Program in Visual Information Processing, Korea University , Seoul , Korea.
authors.forename: Young Bin
authors.initials: YB
authors.lastname: Kim
authors.affiliation: School of Games, Hongik University , Seoul , Korea.
authors.forename: Shin Jin
authors.initials: SJ
authors.lastname: Kang
authors.affiliation: Department of Computer and Radio Communications Engineering, Korea University , Seoul , Korea.
authors.forename: Sang Hyeok
authors.initials: SH
authors.lastname: Lee
authors.affiliation: School of Games, Hongik University , Seoul , Korea.
authors.forename: Jang Young
authors.initials: JY
authors.lastname: Jung
authors.affiliation: Interdisciplinary Program in Visual Information Processing, Korea University , Seoul , Korea.
authors.forename: Hyeong Ryeol
authors.initials: HR
authors.lastname: Kam
authors.affiliation: Department of Computer and Radio Communications Engineering, Korea University , Seoul , Korea.
authors.forename: Jung
authors.initials: J
authors.lastname: Lee
authors.affiliation: Department of Computer and Radio Communications Engineering, Korea University , Seoul , Korea.
authors.forename: Young Sun
authors.initials: YS
authors.lastname: Kim
authors.affiliation: AI Lab, NCSOFT , Seongnam , Korea.
authors.forename: Joonsoo
authors.initials: J
authors.lastname: Lee
authors.affiliation: Department of Computer and Radio Communications Engineering, Korea University , Seoul , Korea.
authors.forename: Chang Hun
authors.initials: CH
authors.lastname: Kim
keywords: Game environments; Outlier detection; User behavior analysis