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곽근창(Keun-Chang Kwak),이명원(Myung-Won Lee),반성범(Sung-Bum Pan) 한국정보기술학회 2012 한국정보기술학회논문지 Vol.10 No.4
In this paper, we present a new facial expression recognition method based on tensor-based presentation from video. Facial expression recognition is a core technique among Human-Robot Interaction (HRI) technology that can naturally interact between robot and human for intelligent service robots. For this purpose, we use Tensor-based Multilinear Principal Component Analysis (TMPCA) to recognize simultaneously facial expression from face images with successive frames under robot environment. This approach extracts feature vector directly from third-order tensor representation considering face images and time axis rather than the well-known vector or second-order tensor representation. The experimental analysis is performed by Manhattan distance, Euclidean distance, and cosine similarity. The experimental results on facial expression database reveal that the presented TMPCA method shows a better performance in comparison with the conventional PCA method.
A Design of Granular Model Using Conditional Clustering and Density Peaks
Keun-Chang Kwak(곽근창) 한국정보기술학회 2015 한국정보기술학회논문지 Vol.13 No.7
In this paper, an improved conditional clustering method based on rapid search of density peaks to construct granular model is proposed. This clustering generates linguistic contexts in the output space and estimates the cluster centers so that possess the homogeneity between input and output space. Furthermore, it can obtain the valid number of cluster corresponding to each context by rapidly searching density peaks using the correlation between local density and distance index from high density points. Thus, this proposed clustering approach can be used as if-then rules with unique characteristics of granular model. It also has the prediction performance by the model output with uncertainty. For this, the experiments are performed on simple synthetical data sets and real-world application problem to demonstrate the superiority and effectiveness through the efficient rule extraction and design of granular model.
곽근창(Keun-Chang Kwak) 한국정보기술학회 2009 한국정보기술학회논문지 Vol.7 No.1
This paper is concerned with three-dimensional object recognition by TSA (Tensor Subspace Analysis). The traditional well-known PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) frequently used in conjunction with face and object recognition consider as a high dimensional vector represented by image, while TSA considers an image as the second order tensor. The relationship between the column and row vectors of the image matrix can be naturally characterized by TSA. Furthermore, TSA finds the intrinsic geometrical structure of the tensor space by learning a lower dimensional tensor subspace. The experimental results on COIL(Columbia object image library) object database used in this paper demonstrate that TSA showed a good recognition rate in comparison with that of PCA and LDA.