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Game Outlier Behavior Detection System Based on Dynamic Time Warp Algorithm

Computer Modeling in Engineering & Sciences 2022年第4期130卷 219-237页

作者： Shinjin Kang Soo Kyun Kim School of Games Hongik UniversitySejong30016Korea Department of Computer Engineering Jeju National UniversityJeju63243Korea

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

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Outlier Behavior Detection for Indoor Environment Based on t-SNE Clustering

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Computers, Materials & Continua 2021年第9期68卷 3725-3736页

作者： Shinjin Kang Soo Kyun Kim School of Games Hongik UniversitySejong30016Korea Department of Computer Engineering Jeju National UniversityJeju63243Korea

In this study,we propose a low-cost system that can detect the space outlier utilization of residents in an indoor environment.We focus on the users’app usage to analyze unusual behavior,especially in indoor spaces.This is reflected in the behavioral analysis in that the frequency of using smartphones in personal spaces has recently increased.Our system facilitates autonomous data collection from mobile app logs and Google app servers and generates a high-dimensional dataset that can detect outlier behaviors.The density-based spatial clustering of applications with noise(DBSCAN)algorithm was applied for effective singular movement analysis.To analyze high-level mobile phone usage,the t-distributed stochastic neighbor embedding(t-SNE)algorithm was employed.These two clustering algorithms can effectively detect outlier behaviors in terms of movement and app usage in indoor spaces.The experimental results showed that our system enables effective spatial behavioral analysis at a low cost when applied to logs collected in actual living spaces.Moreover,large volumes of data required for outlier detection can be easily acquired.The system can automatically detect the unusual behavior of a user in an indoor space.In particular,this study aims to reflect the recent trend of the increasing use of smartphones in indoor spaces to the behavioral analysis.

关键词： Outlier detection trajectory clustering behavior analysis app data smartphone

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3D Face Reconstruction from a Single Image Using a Combined PCA-LPP Method

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Computers, Materials & Continua 2023年第3期74卷 6213-6227页

作者： Jee-Sic Hur Hyeong-Geun Lee Shinjin Kang Yeo Chan Yoon Soo Kyun Kim Department of Computer Engineering Jeju National UniversityJeju63243Korea School of Games Hongik UniversitySejong30016Korea Department of Artificial Intelligence Jeju National UniversityJeju63243Korea

In this paper, we proposed a combined PCA-LPP algorithm toimprove 3D face reconstruction performance. Principal component analysis(PCA) is commonly used to compress images and extract features. Onedisadvantage of PCA is local feature loss. To address this, various studies haveproposed combining a PCA-LPP-based algorithm with a locality preservingprojection (LPP). However, the existing PCA-LPP method is unsuitable for3D face reconstruction because it focuses on data classification and clustering.In the existing PCA-LPP, the adjacency graph, which primarily shows the connectionrelationships between data, is composed of the e-or k-nearest neighbortechniques. By contrast, in this study, complex and detailed parts, such aswrinkles around the eyes and mouth, can be reconstructed by composing thetopology of the 3D face model as an adjacency graph and extracting localfeatures from the connection relationship between the 3D model vertices.Experiments verified the effectiveness of the proposed method. When theproposed method was applied to the 3D face reconstruction evaluation set,a performance improvement of 10% to 20% was observed compared with theexisting PCA-based method.

关键词： Principal component analysis locality preserving project 3DMM face reconstruction face modeling

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