• 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 the Institute of Electronics Engineers of Korea SP
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• v.40 no.6
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• pp.149-156
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• 2003

This paper proposes a face recognition method based on modified Otsu binarization, Hu moment and linear discriminant analysis (LDA). Proposed method is robust to brightness, contrast, scale, rotation, and translation changes. Modified Otsu binarization can make binary images that have the invariant characteristic in brightness and contrast changes. From edge and multi-level binary images obtained by the threshold method, we compute the 17 dimensional Hu moment and then extract feature vector using LDA algorithm. Especially, our face recognition system is robust to scale, rotation, and translation changes because of using Hu moment. Experimental results showed that our method had almost a superior performance compared with the conventional well-known principal component analysis (PCA) and the method combined PCA and LDA in the perspective of brightness, contrast, scale, rotation, and translation changes with Olivetti Research Laboratory (ORL) database and the AR database.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.299-301
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• 2005

본 연구는 생체정보를 이용하여 개인을 인증하고 확인하기 위한 방법으로 기존 단일 생체인식 기법의 단점을 보완하기 위해 홍채와 얼굴을 이용한 다중생체인식(Multi-Modal Biometrics Recognition)기법을 연구하였다. 중국 홍채 데이터베이스 CASIA(Chinese Academy of Science)에 Gabor Wavelet과 FLDA(Fuzzy Linear Discriminant Analysis)를 사용하여 특징벡터를 획득하였으며, FERET(FERET(Face Recognition Technology) 얼굴영상데이터를 사용하여 FERET 연구에서 매우 우수한 성능을 보인 EBGM알고리듬으로 특징벡터를 획득하였다. 이로부터 얻어진 두 score 값에 대하여 다양한 균등화 과정을 시도해 보았으며, 등록자와 침입자를 구분하기 위한 Fusion Algorithm으로 Bayesian Classifier, Support vector machine, Fisher's linear discriminant를 사용하였다. 또한, 널리 사용되는 방법 중 Weighted Summation을 이용하여 다중생체인식의 성능을 비교해 보았다.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1960-1961
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• 2011

얼굴 인식에서 가장 많이 사용되고 있는 PCA(Principal Component Analysis)는 고차원의 얼굴 데이터를 낮은 차원으로 표현할 수 있다는 장점이 있다. LDA(Linear Discriminant Analysis)는 서로 다른 데이터를 잘 분리할 수 있으며, 얼굴 인식에서 우수한 성능을 보인다. 본 연구에서는 서로의 장점을 결합하여 PCA와 LDA를 혼합, 적용하였다. 고차원의 얼굴데이터를 PCA로 차원 축소한 후 LDA를 이용해 더욱 효과적인 분류가 되어 얼굴 인식률을 향상시킨다. 인식 모듈로는 pRBFNN(Polynomial Based Radial Basis Function Neural Networks) 모델을 구축하여 고차원 패턴인식 문제에 대한 해결책을 제시하고자 한다. 그리고 제안된 패턴분류기는 얼굴 데이터를 사용하여 성능을 확인한다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.757-762
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• 2012

In this paper, the multi feature extraction algorithm for estimation of wrist movements based on Electromyogram(EMG) is proposed. For the extraction of precise features from the EMG signals, the difference absolute mean value(DAMV), the mean absolute value(MAV), the root mean square(RMS) and the difference absolute standard deviation value(DASDV) to consider amplitude characteristic of EMG signals are used. We figure out a more accurate feature-set by combination of two features out of these, because of multi feature extraction algorithm is more precise than single feature method. Also, for the motion classification based on EMG, the linear discriminant analysis(LDA), the quadratic discriminant analysis(QDA) and k-nearest neighbor(k-NN) are used. We implemented a test targeting twenty adult male to identify the accuracy of EMG pattern classification of wrist movements such as up, down, right, left and rest. As a result of our study, the LDA, QDA and k-NN classification method using feature-set with MAV and DASDV showed respectively 87.59%, 89.06%, 91.75% accuracy.

• 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.

• Journal of Korea Game Society
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• v.13 no.4
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• pp.15-24
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• 2013

The game of Go, which is originated at least more than 2,500 years ago, is one of the oldest board games in the world. So far the theoretical studies concerning to the Go openings are still insufficient. We applied traditional LDA algorithm to recognize a pro player's opening to a class obtained from the training openings. Both class-independent LDA and class-dependent LDA methods are conducted with the Go game records of the Korean top 10 professional Go players. Experimental result shows that the average recognition rate of class-independent LDA is 14% and class-dependent LDA 12%, respectively. Our research result also shows that in contrary to our common sense the algorithm based on PCA outperforms the algorithm based on LDA and reveals the new fact that the Euclidean distance metric method rarely does not inferior to LDA.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.40-49
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• 2018

We propose a more stable robust recognition algorithm which detects faces reliably even in cases where there are changes in lighting and angle of view, as well it satisfies efficiency in calculation and detection performance. We propose detects the face area alone after normalization through pre-processing and obtains a feature vector using (PCA). The feature vector is applied to LDA and using Euclidean distance of intra-class variance and inter class variance in the 2nd dimension, the final analysis and matching is performed. Experimental results show that the proposed method has a wider distribution when the input image is rotated $45^{\circ}$ left / right. We can improve the recognition rate by applying this feature value to a single algorithm and complex algorithm, and it is possible to recognize in real time because it does not require much calculation amount due to dimensional reduction.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.4
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• pp.737-742
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• 2013

The surveillance system has been developed to be intelligent which can judge and cope by itself using human recognition technique. The existing face recognition is excellent at a short distance but recognition rate is reduced at a long distance. In this paper, we analyze the performance of face recognition according to interpolation and face recognition algorithm in face recognition using the multiple distance face images to training. we use the nearest neighbor, bilinear, bicubic, Lanczos3 interpolations to interpolate face image and PCA and LDA to face recognition. The experimental results show that LDA-based face recognition with bilinear interpolation provides performance in face recognition.

• Proceedings of the KIEE Conference
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• 2005.07c
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• pp.2079-2081
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• 2005

Electrical treeing occurrence is caused by some defect in solid insulator. Those are accompany the PD(partial discharge) occurrence. And lifetime of the insulator is affected by PD. So, detection of electrical treeing is important thing as this view. Especially, detection of the end treeing is more important and have meaning for industrial engineering because concerned with maintenance and replacement of equipment. In this paper, evaluation of treeing process were studied and PCA(principle component analysis)-LDA(linear discriminant analysis) as classification method were used. The result is present the good recognition.