• The Journal of the Korea Contents Association
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• v.10 no.8
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• pp.18-25
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• 2010

The study has proposed a method that simultaneously takes advantage of each projection matrix acquired by using column-directional two-dimensional PCA(C2DPCA) and row-directional two-dimensional LDA(R2DLDA). The proposed method can acquire a great secure recognition rate, with no relation to the number of training images, with acquired low-dimensional feature matrixes including both the horizontal and the vertical features of a face. Besides, in the alternate experiment of PCA and LDA to row-direction and column-direction respectively(C2DPCA & R2DLDA, C2DLDA & R2DPCA), we could make sure the system of 2 dimensional LDA with row-directional feature(C2DPCA & R2DLDA) obtain higher recognition rate with low dimension than opposite case. As a result of experimenting that, the proposed method has showed a greater recognition rate of 99.4% than the existing methods such as 2DPCA and 2DLDA, etc. Also, it was proved that its recognition processing is over three times as fast as that of 2DPCA or 2DLDA.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.648-654
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• 2009

In this paper, a method called bilateral diagonal 2DLDA is proposed for face recognition. Two methods called Dia2DPCA and Dia2DLDA were suggested to reserve the correlations between the variations in the rows and columns of diagonal images. However, these methods work in the row direction of these images. A row-directional projection matrix can be obtained by calculating the between-class and within-class covariance matrices making an allowance for the column variation of alternative diagonal face images. In addition, column-directional projection matrix can be obtained by calculating the between-class and within-class covariance matrices making an allowance for the row variation in diagonal images. A bilateral projection scheme was applied using left and right multiplying projection matrices. As a result, the dimension of the feature matrix and computation time can be reduced. Experiments carried out on an ORL face database show that the proposed method with three different distance measures, namely, Frobenius, Yang and AMD, is more accurate than some methods, such as 2DPCA, B2DPCA, 2DLDA, etc.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.12C
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• pp.1162-1166
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• 2005

A new low dimensional feature representation technique is presented in this paper. Linear discriminant analysis is a popular feature extraction method. However, in the case of high dimensional data, the computational difficulty and the small sample size problem are often encountered. In order to solve these problems, we propose two dimensional direct LDA algorithm, which directly extracts the image scatter matrix from 2D image and uses Direct LDA algorithm for face recognition. The ORL face database is used to evaluate the performance of the proposed method. The experimental results indicate that the performance of the proposed method is superior to DLDA.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.10C
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• pp.942-947
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• 2006

In this paper, a new feature representation technique called Improved 2-directional 2-dimensional direct linear discriminant analysis (Improved $(2D)^2$ DLDA) is proposed. In the case of face recognition, thesmall sample size problem and need for many coefficients are often encountered. In order to solve these problems, the proposed method uses the direct LDA and 2-directional image scatter matrix. Moreover the selection method of feature vector and the method of similarity measure are proposed. The ORL face database is used to evaluate the performance of the proposed method. The experimental results show that the proposed method obtains better recognition rate and requires lesser memory than the direct LDA.

• ETRI Journal
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• v.31 no.4
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• pp.438-447
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• 2009

In this paper, a novel image projection technique for face recognition application is proposed which is based on linear discriminant analysis (LDA) combined with the relevance-weighted (RW) method. The projection is performed through 2-directional and 2-dimensional LDA, or $(2D)^2$LDA, which simultaneously works in row and column directions to solve the small sample size problem. Moreover, a weighted discriminant hyperplane is used in the between-class scatter matrix, and an RW method is used in the within-class scatter matrix to weigh the information to resolve confusable data in these classes. This technique is called the relevance-weighted $(2D)^2$LDA, or RW$(2D)^2$LDA, which is used for a more accurate discriminant decision than that produced by the conventional LDA or 2DLDA. The proposed technique has been successfully tested on four face databases. Experimental results indicate that the proposed RW$(2D)^2$LDA algorithm is more computationally efficient than the conventional algorithms because it has fewer features and faster times. It can also improve performance and has a maximum recognition rate of over 97%.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.157-163
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• 2005

In this paper, we present the multimodal system based on the fusion of two user-friendly biometric modalities: Iris and Face. In order to reach robust identification and verification we are going to combine two different biometric features. we specifically apply 2-D discrete wavelet transform to extract the feature sets of low dimensionality from iris and face. And then to obtain Reduced Joint Feature Vector(RJFV) from these feature sets, Direct Linear Discriminant Analysis (DLDA) is used in our multimodal system. In addition, the Synergetic Neural Network(SNN) is used to obtain matching score of the preprocessed data. This system can operate in two modes: to identify a particular person or to verify a person's claimed identity. Our results for both cases show that the proposed method leads to a reliable person authentication system.

• Journal of Acupuncture Research
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• v.28 no.3
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• pp.85-100
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• 2011

Objectives : The purpose of this thesis is to help the preparation of oriental medicine clinical guidelines for drawing up the standards of oriental medicine demonstration and diagnosis classification about the neck pain. Methods : Statistical analysis about Gyeonghangtong(頸項痛), Nakchim(落枕), Sagyeong(斜頸), Hanggang (項强) classified experts' opinions about neck pain patients by Delphi method is conducted by using oriental medicine diagnosis questionnaire. The result was classified by using linear discriminant analysis (LDA), diagonal linear discriminant analysis (DLDA), diagonal quadratic discriminant analysis (DQDA), K-nearest neighbor classification (KNN), classification and regression trees (CART), support vector machines (SVM). Results : The results are summarized as follows. 1. The result analyzed by using LDA has a hit rate of 84.47% in comparison with the original diagnosis. 2. High hit rate was shown when the test for three categories such as Gyeonghangtong and Hanggang category, Sagyeong caterogy and Nakchim caterogy was conducted. 3. The result analyzed by using DLDA has a hit rate of 58.25% in comparison with the original diagnosis. The result analyzed by using DQDA has a accuracy of 57.28% in comparison with the original diagnosis. 4. The result analyzed by using KNN has a hit rate of 69.90% in comparison with the original diagnosis. 5. The result analyzed by using CART has a hit rate of 69.60% in comparison with the original diagnosis. There was a hit rate of 70.87% When the test of selected 8 significant questions based on analysis of variance was performed. 6. The result analyzed by using SVM has a hit rate of 80.58% in comparison with the original diagnosis. Conclusions : Statistical analysis using oriental medicine diagnosis questionnaire on neck pain generally turned out to have a significant result.

• Journal of Acupuncture Research
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• v.29 no.5
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• pp.61-74
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• 2012

Objectives : This study is perfomed for preparation of oriental medicine clinical guidelines for drawing up the standards of oriental medicine demonstration and diagnosis classification about the knee pain. Methods : Statistical analysis about Crane's-knee wind(鶴膝風), arthralgia syndrome(痺症), knee injury(膝傷), gout arthritis(痛風), Youk jeol poung(歷節風) classified experts' opinions about knee pain patients by Delphi method is conducted by using oriental medicine diagnosis questionnaire. The result was classified by using linear discriminant analysis(LDA), diagonal linear discriminant analysis(DLDA), diagonal quadratic discriminant analysis(DQDA), K-nearest neighbor classification(KNN), classification and regression trees(CART), support vector machines(SVM). Results : The results are summarized as follows. 1. The result analyzed by using LDA has a hit rate of 81.65% in comparison with the original diagnosis. 2. The result analyzed by using DLDA has a hit rate of 63.3% in comparison with the original diagnosis. 3. The result analyzed by using DQDA has a hit rate of 65.14% in comparison with the original diagnosis. 4. The result analyzed by using KNN has a hit rate of 74.31% in comparison with the original diagnosis. 5. The result analyzed by using CART has a hit rate of 75.23% in comparison with the original diagnosis when the test of selected 13 significant questions based on analysis of variance was performed. 6. The result analyzed by using SVM has a hit rate of 87.16% in comparison with the original diagnosis. Conclusions : Statistical analysis using oriental medicine diagnosis questionnaire on knee pain generally turned out to have a significant result.