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

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.

User Interface-Based Repeated Sequence Detection Method for Authentication

In this paper,we propose an authentication method that use mouse and keystroke dynamics to enhance online privacy and security.The proposed method identifies personalized repeated user interface(UI)sequences by analyzing mouse and keyboard data.To this end,an Apriori algorithm based on the keystroke-level model(KLM)of the human–computer interface domain was used.The proposed system can detect repeated UI sequences based on KLM for authentication in the software.The effectiveness of the proposed method is verified through access test-ing using commercial applications that require intensive UI interactions.The results show using our cognitive mouse-and-keystroke dynamics system can com-plement authentication at the application level.

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

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.

Image Translation Method for Game Character Sprite Drawing

Two-dimensional(2D)character animation is one of the most important visual elements on which users’interest is focused in the game field.However,2D character animation works in the game field are mostly performed manually in two dimensions,thus generating high production costs.This study proposes a generative adversarial network based production tool that can easily and quickly generate the sprite images of 2D characters.First,we proposed a methodology to create a synthetic dataset for training using images from the real world in the game resource production field where machine learning datasets are insufficient.In addition,we have enabled effective sprite generation while minimizing user input in the process of using the tool.To this end,we proposed a mixed input method with a small number of segmentations and skeletal bone paintings.The proposed image-to-image translation network effectively generated sprite images from the user input images using the skeletal loss.We conducted an experiment regarding the number of images required and showed that 2D sprite resources can be generated even with a small number of segmentation inputs and one skeletal bone drawing.

Game Outlier Behavior Detection System Based on Dynamic Time Warp Algorithm

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.