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Newly Regularized LDA for SSS Problem with application to Face Recognition
곽려혜(Lee Hui Kueh),김권우(Kwon-Woo Kim),한동열(Dong-Yeol Han),이승호(Seung-Ho Lee),이준탁(John-Tark Lee),이권순(Kwon-Soon Lee) 대한전기학회 2010 대한전기학회 학술대회 논문집 Vol.2010 No.7
A newly proposed FR (Face Recognition) approach with a weighted regularization parameter based on the conventional R-LDA (Regularized Linear D iscriminant A nalysis) method was presented in this paper. SSS (Sm all Sample Size) problem refers to the total number of training samples is less than the dimension of face feature space and all the scatter matrices of LDA are singular. Therefore, it is impossible to apply the LDA algorithm to the FR. In this paper, it was attempted to optimize the revised Fisher's criterion with a weighted regularization parameter as a solution of the SSS problem. Simulations using ORL (Olivetti Research Lab) database in MATLAB were simulated in order to evaluate the recognition performance of the proposed FR. In addition, the recognition performance of the proposed approach was compared to the ones of the well-known conventional methods such as Eigenfaces and R-LDA, which were established in this paper.
Lee Hui Kueh,John-Tark Lee,Kwon-Soon Lee 한국지능정보시스템학회 2009 한국지능정보시스템학회 학술대회논문집 Vol.2009 No.11
A face recognition based on Regularized LDA is presented. The recognition performance is demonstrated in Matlab using face database of Intelligent Control Laboratory (ICONL). The algorithm attempts to address the SSS problem using a regularized Fisher's separability criterion. The simulations are conducted which identified the test samples as belonging to the same subject of those of trained subject of the ICONL face database. In addition, the recognition performance is compared to the recognition performance of the original PCA which are demonstrated in the paper. The main idea of the paper is to demonstrate the proposed method is particularly robust against the SSS problem compared to the traditional LDA and the simulations results show that the proposed algorithm improves the robustness of the recognition performance compared to PCA.
이승호(Seung-Ho Lee),곽려혜(Lee-Hui Kueh),김권우(Kwon-Woo Kim),한동열(Dong-yeol Han),이준탁(John-Tark Lee) 대한전기학회 2010 대한전기학회 학술대회 논문집 Vol.2010 No.7
This paper is design of synchronous machine using matlab. I did description of a basic attribute numerical formula of a synchronous machine. I used matlab in the above and calculated basic data of the synchronous machine. The input data which I calculated with matlab is the voltage, frequency, power factor, power. and, the output data calculated design size data and data of an efficiency, loss of etc.
A Study on Face Recognition Using PCA
Joon-Tark Lee,Lee-Hui Kueh 동아대학교 정보기술연구소 2006 情報技術硏究所論文誌 Vol.14 No.1
In this paper, a face recognition algorithm system using Principle Component Analysis is proposed. The algorithm recognized a person by comparing characteristics (features) of the face to those of known individuals which is a face database of Intelligence Control Laboratory (ICONL). Experiments were simulated in order to demonstrate the performance of this algorithm due to face recognition which presented for the classification of face and non-face and the classification of known and unknown.
A Study on the Face Recognition Using PCA
Joon-Tark Lee,Lee Hui Kueh 한국지능시스템학회 2006 한국지능시스템학회 학술발표 논문집 Vol.16 No.2
In this paper, a face recognition algorithm system using Principle Component Analysis is proposed. The algorithm recognized a person by comparing characteristics (features) of the face to those of known individuals which is a face database of Intelligence Control Laboratory (ICONL). Experiments were simulated in order to demonstrate the performance of this algorithm due to face recognition which presented for the classification of face and non-face and the classification of known and unknown.
A Study on the Face Recognition Using PCA Algorithm
이준탁(John-Tark Lee),곽려혜(Lee-Hui Kueh) 한국지능시스템학회 2007 한국지능시스템학회논문지 Vol.17 No.2
In this paper, a face recognition algorithm system using Principal Component Analysis (PCA) is proposed. The algorithm recognized a person by comparing characteristics (features) of the face to those of known individuals of Intelligent Control Laboratory (ICONL) face database. Simulations are carried out to investigate the algorithm recognition performance, which classified the face as a face or non-face and then classified it as known or unknown one. Particularly, a Principal Components of Linear Discriminant Analysis (PCA + LDA) face recognition algorithm is also proposed in order to confirm the recognition performances and the adaptability of a proposed PCA for a certain specific system.