• Journal of Korea Multimedia Society
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• v.13 no.6
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• pp.817-824
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• 2010

Linear Discriminant Analysis (LDA) has been successfully applied for dimension reduction in face recognition. However, LDA requires the transformation of a face image to a one-dimensional vector and this process can cause the correlation information among neighboring pixels to be disregarded. On the other hand, 2D-LDA uses 2D images directly without a transformation process and it has been shown to be superior to the traditional LDA. Nevertheless, there are some problems in 2D-LDA. First, it is difficult to determine the optimal number of feature vectors in a reduced dimensional space. Second, the size of rectangular windows used in 2D-LDA makes strong impacts on classification accuracies but there is no reliable way to determine an optimal window size. In this paper, we propose a new algorithm to overcome those problems in 2D-LDA. We adopt an ensemble approach which combines several classifiers obtained by utilizing various window sizes. And a practical method to determine the number of feature vectors is also presented. Experimental results demonstrate that the proposed method can overcome the difficulties with choosing an optimal window size and the number of feature vectors.

• Journal of Korea Multimedia Society
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• v.17 no.12
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• pp.1446-1452
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• 2014

Existing LDA uses the transform matrix that maximizes distance between classes. So we have to convert from an image to one-dimensional vector as training vector. However, in 2D-LDA, we can directly use two-dimensional image itself as training matrix, so that the classification performance can be enhanced about 20% comparing LDA, since the training matrix preserves the spatial information of two-dimensional image. However 2D-LDA uses same calculation schema for transformation matrix and therefore both LDA and 2D-LDA has the heteroscedastic problem which means that the class classification cannot obtain beneficial information of spatial distances of class clusters since LDA uses only data correlation-based covariance matrix of the training data without any reference to distances between classes. In this paper, we propose a new method to apply training matrix of 2D-LDA by using WPS-LDA idea that calculates the reciprocal of distance between classes and apply this weight to between class scatter matrix. The experimental result shows that the discriminating power of proposed 2D-LDA with weighted between class scatter has been improved up to 2% than original 2D-LDA. This method has good performance, especially when the distance between two classes is very close and the dimension of projection axis is low.

• 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.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2991-3007
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• 2022

Two dimensional locality preserving projections (2D-LPP) is an improved algorithm of 2D image to solve the small sample size (SSS) problems which locality preserving projections (LPP) meets. It's able to find the low dimension manifold mapping that not only preserves local information but also detects manifold embedded in original data spaces. However, 2D-LPP is simple and elegant. So, inspired by the comparison experiments between two dimensional linear discriminant analysis (2D-LDA) and linear discriminant analysis (LDA) which indicated that matrix based methods don't always perform better even when training samples are limited, we surmise 2D-LPP may meet the same limitation as 2D-LDA and propose a novel matrix exponential method to enhance the performance of 2D-LPP. 2D-MELPP is equivalent to employing distance diffusion mapping to transform original images into a new space, and margins between labels are broadened, which is beneficial for solving classification problems. Nonetheless, the computational time complexity of 2D-MELPP is extremely high. In this paper, we replace some of matrix multiplications with multiple multiplications to save the memory cost and provide an efficient way for solving 2D-MELPP. We test it on public databases: random 3D data set, ORL, AR face database and Polyu Palmprint database and compare it with other 2D methods like 2D-LDA, 2D-LPP and 1D methods like LPP and exponential locality preserving projections (ELPP), finding it outperforms than others in recognition accuracy. We also compare different dimensions of projection vector and record the cost time on the ORL, AR face database and Polyu Palmprint database. The experiment results above proves that our advanced algorithm has a better performance on 3 independent public databases.

• Proceedings of the KSME Conference
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• 2001.11b
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• pp.371-376
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• 2001

The operating point of a small-sized axial fan for refrigerator is strongly dependent upon the system resistance. Therefore, the turbulent flow characteristics around a small-sized axial fan may change significantly according to the operating point. This study represents three-dimensional turbulent flow characteristics around a small-sized axial fan measured at the four operating points such as $\varphi=0.1$, 0.18, 0.25 and 0.32 by using fiber-optic type LDA system. This LDA system is composed of a 5 W Argon-ion laser, two optics in back-scatter mode, three BSA's, a PC, and a three-dimensional automatic traversing system. A kind of paraffin fluid is utilized for supplying particles by means of fog generator. Mean velocity profiles downstream of a small-sized axial fan along the radial distance show that both the streamwise and the tangential components exist predominantly in downstream except $\varphi=0.1$ and have a maximum value at the radial distance ratio of about 0.8, but the radial component, which its velocity is relatively small, is acting role that only turns flow direction to the outside or the central part of axial fan. Moreover, all of the velocity components downstream at $\varphi=0.1$ show much smaller than those upstream due to the static pressure rise at the low-flowrate region.

• Journal of Power System Engineering
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• v.4 no.3
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• pp.25-33
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• 2000

This study represents three-dimensional turbulent flow characteristics around an axial fan measured at the operating point ${\varphi}=0.32$, which is equivalent to the maximum flowrate region, by using three-dimensional fiber-optic type LDA system. This LDA system is composed of a 5 W Argon-ion laser, two optics in back-scatter mode, three BSA's, a PC, and a three-dimensional automatic traversing system. A kind of paraffin fog is used for laser particles in this study. Mean velocity profiles around an axial fan along the downstream radial distance show that the streamwise and the tangential components exist as a predominant velocity and have the maximum value at the radial distance ratio 0.8, while the radial component has a small scale distribution and its flow direction is inward except a part of blade tip. The turbulent intensity profiles show that the radial component exists the most greatly. And also the turbulent kinetic energy shows about 60% as a maximum value at the radial distance ratio 0.9. Moreover, the Reynolds shear stresses do not exist at upstream flow, but the streamwise and the radial components of them show about 20% as a maximum value at the radial distance ratio 0.9 at downstream flow.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.25 no.6
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• pp.819-828
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• 2001

The operating point of a small-sized axial fan is strongly dependent upon the system resistance. Therefore, the turbulent flow characteristics around a small-sized axial fan may change significantly according to the operating point. This study represents three-dimensional turbulent flow characteristics around a small-sized axial fan measured at the ideal design point $\phi$=0.25, which is equivalent to the maximum total efficiency point, by using three dimensional fiber-optic type LDA system. This LDA system is composed of a 5 W Argon-ion laser, two optics in back-scatter mode, three BSAs, a PC, and a three-dimensional automatic traversing system. A kind of paraffin fluid is used to supply particles by means of fog generator. Mean velocity profiles downstream of a small-sized axial fan along the radial distance show that the streamwise and the tangential components exist in a predominant manner, while the radial component has a small scale distribution and shows the inflection which its flow direction is inward or outward. Moreover, the turbulent intensity profiles show that the radial component exists the most greatly among turbulent energies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.4
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• pp.897-904
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• 2005

Face Recognition has been an active research area because it is not difficult to acquire face image data and it is applicable in wide range area in real world. Due to the high dimensionality of a face image space, however, it is not easy to process the face images. In this paper, we propose a method to reduce the dimension of the facial data and extract the features from them. It will be solved using the method which extracts the features from holistic face images. The proposed algorithm consists of two parts. The first is the using of principal component analysis (PCA) to transform three dimensional color facial images to one dimensional gray facial images. The second is integrated linear discriminant analusis (PCA+LDA) to prevent the loss of informations in case of performing separated steps. Integrated LDA is integrated algorithm of PCA for reduction of dimension and LDA for discrimination of facial vectors. First, in case of transformation from color image to gray image, PCA(Principal Component Analysis) is performed to enhance the image contrast to raise the recognition rate. Second, integrated LDA(Linear Discriminant Analysis) combines the two steps, namely PCA for dimensionality reduction and LDA for discrimination. It makes possible to describe concise algorithm expression and to prevent the information loss in separate steps. To validate the proposed method, the algorithm is implemented and tested on well controlled face databases.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9C
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• pp.748-755
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• 2010

In this paper we propose an improved face recognition method using Gabor tensor. Gabor transform is known to be able to represent characteristic feature in face and reduced environmental influence. It may contribute to improve face recognition ratio. We attempted to combine three-dimensional tensor from Gabor transform with MPCA(Multilinear PCA) and LDA. MPCA with tensor which use various features is more effective than traditional one or two dimensional PCA. It is known to be robust to the change of face expression or light. Proposed method is simulated by MATALB9 using ORL and Yale face database. Test result shows that recognition ratio is improved maximum 9~27% compared with exisisting face recognition method.