• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1147-1155
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• 2005

This paper deals with Iris recognition as one of biometric techniques which is applied to identify a person using his/her behavior or congenital characteristics. The Iris of a human eye has a texture that is unique and time invariant for each individual. First, we obtain the feature vector from the 2D Iris pattern having a property of size invariant and using the fuzzy LDA which is further through four types of 2D Gabor wavelet. At the recognition process, we compute the similarity measure based on the correlation values. Here, since we use four different matching values obtained from four different directional Gabor wavelet and select the maximum value, it is possible to minimize the recognition error rate. To show the usefulness of the proposed algorithm, we applied it to a biometric database consisting of 300 Iris Patterns extracted from 50 subjects and finally got more higher than $90\%$ recognition rate.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.538-540
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• 2002

본 논문에서는 실시간 카메라 입력 영상에 적합한 얼굴 검출을 위해 다양한 외부적 환경에 덜 민감한 새로운 알고리즘을 제안한다. 빛이나 조명의 영향에 의한 오류를 방지하기 위해 전처리 과정을 포함시키고 형판 정합방법의 단점을 개선하기 위해 얼굴 인식에서 주로 쓰이는 방법인 주성분 분석(PCA :Principal Component Analyses) 변환을 적용하고. 생성된 주성분(Principal Component)을 선형 판별 분석(LDA: Linear Discriminant Analysis)의 입력으로 사용하는 방법을 통해 얼굴을 검출하도록 하였다. 실험을 위해 실제 환경과 같은 6개 카테고리의 동영상을 중심으로 실험한 결과, 본 논문에서 제안하는 방법이 기존의 PCA만을 이용한 방법보다 좋은 성능을 보여줌을 알 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.718-720
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• 2004

얼굴 인증에서 가장 보편적으로 사용되고 있는 주성분 분석(PCA . Principal Component Analysis)은 정면 얼굴과 같은 특징 패턴에 대해서 비교적 높은 성능을 보인다. 인식률을 떨어뜨리지 않으면서 데이터량을 줄일 수 있는 효과가 있어 클래스를 잘 축약하여 표현하기에 유용하다. 하지만 조명이나 표정의 변화에 대해서는 성능을 보장할 수 없다 이를 보완하기 위해 성분이 다른 클래스간의 분리가 수월하도록 선형판별분석(LDA Linear Discriminant Analysis)을 사용한다 LDA는 데이터의 양이 적을 때는 성능이 떨어지는 단점이 있다 그래서 PCA와 LDA를 융합한 기술을 사용하면 더 나은 성능을 얻을 수 있는데 Min, Max, Mean, Append, Majority voting방법 등이 이에 해당된다. 하지만 기존 연구에서는 제한적 데이터베이스에 대한 실험에 그쳐 실험 결과의 객관성이 부족했다. 본 논문에서는 정형화된 환경에서 여러 가지 데이터베이스를 사용해 실험함으로써 Min, Max, Mean 융합 알고리즘의 성능을 비교 분석한다. 융합 알고리즘이 언제나 좋은 성능을 내는 것은 아니지만 얼굴영상에서 조명이나 표정 등이 변화함에 상관없이 일정 수준의 인증율을 보장하고 있다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.4
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• pp.42-53
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• 2012

In pattern recognition and machine learning society, classification has been a classical problem and the most widely researched area. Adaptive boosting also known as Adaboost has been successfully applied to binary classification problems. It is a kind of boosting algorithm capable of constructing a strong classifier through a weighted combination of weak classifiers. On the other hand, the PCA and LDA algorithms are the most popular linear feature extraction methods used mainly for dimensionality reduction. In this paper, the combination of Adaboost and feature extraction methods is proposed for efficient classification of two class data. Conventionally, in classification problems, the roles of feature extraction and classification have been distinct, i.e., a feature extraction method and a classifier are applied sequentially to classify input variable into several categories. In this paper, these two steps are combined into one resulting in a good classification performance. More specifically, each projection vector is treated as a weak classifier in Adaboost algorithm to constitute a strong classifier for binary classification problems. The proposed algorithm is applied to UCI dataset and FRGC dataset and showed better recognition rates than sequential application of feature extraction and classification methods.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.5
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• pp.251-260
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• 2016

Face recognition is a technology to extract feature from a facial image, learn the features through various algorithms, and recognize a person by comparing the learned data with feature of a new facial image. Especially, in order to improve the rate of face recognition, face recognition requires various processing methods. In the training stage of face recognition, feature should be extracted from a facial image. As for the existing method of extracting facial feature, linear discriminant analysis (LDA) is being mainly used. The LDA method is to express a facial image with dots on the high-dimensional space, and extract facial feature to distinguish a person by analyzing the class information and the distribution of dots. As the position of a dot is determined by pixel values of a facial image on the high-dimensional space, if unnecessary areas or frequently changing areas are included on a facial image, incorrect facial feature could be extracted by LDA. Especially, if a camera image is used for face recognition, the size of a face could vary with the distance between the face and the camera, deteriorating the rate of face recognition. Thus, in order to solve this problem, this paper detected a facial area by using a camera, removed unnecessary areas using the facial feature area calculated via a Gabor filter, and normalized the size of the facial area. Facial feature were extracted through LDA using the normalized facial image and were learned through the artificial neural network for face recognition. As a result, it was possible to improve the rate of face recognition by approx. 13% compared to the existing face recognition method including unnecessary areas.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.1
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• pp.196-204
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• 2008

In this paper, we propose an efficient method to classify human facial impression using frontal face image. The features that represent the shape of eye, jaw and face are used. The proposed method employs PCA, LDA and SVM in series. PCA is used to project the feature space to a low dimensional subspace. LDA produces well separated classes in a low dimensional subspace even under severe variation. This results in good discriminating power for classification. SVM is used to classify the data. Human face has been classified for 8 facial impressions. The experiments have been performed for many face images, and show encouraging result.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.923-933
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• 2007

In this paper, we present a method for performance enhancement of the marker detection system by using SVM(Support Vector Machine) and LDA(Linear Discriminant Analysis). It converts the input image to a binary image and extracts contours of objects in the binary image. After that, it approximates the contours to a list of line segments. It finds quadrangle by using geometrical features which are extracted from the approximated line segments. It normalizes the shape of extracted quadrangle into exact squares by using the warping technique and scale transformation. It extracts feature vectors from the square image by using principal component analysis. It then checks if the square image is a marker image or a non-marker image by using a SVM classifier. After that, it computes feature vectors by using LDA for the extracted marker images. And it calculates the distance between feature vector of input marker image and those of standard markers. Finally, it recognizes the marker by using minimum distance method. Experimental results show that the proposed method achieves enhancement of recognition rate with smaller feature vectors by using LDA and it can decrease false detection errors by using SVM.

• The KIPS Transactions:PartB
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• v.11B no.7 s.96
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• pp.885-892
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• 2004

In this paper, we propose a multi-modal biometrics system based on face and signature recognition system. Here, the face recognition system is designed by fuzzy LDA, and the signature recognition system is implemented with the LDA and segment matching methods. To effectively aggregate two systems, we obtain statistical distribution models based on matching values for genuine and impostor, respectively. And then, the final verification is Performed by the support vector machine. From the various experiments, we find that the proposed method shows high recognition rates comparing with the conventional methods.

• Analytical Science and Technology
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• v.34 no.3
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• pp.134-141
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• 2021

In this study, the fatty acid (FA) composition of commercial sesame oils (n = 62) was investigated using gas chromatography with flame ionization detector (GC-FID). Multivariate statistical techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA), were applied to the chromatographic data of the FAs to discriminate the geographical origin of sesame oils. A statistically significant difference was observed in the content of C16:0, C18:0, C18:1, and C18:2 between domestic and imported sesame oils. A satisfactory recovery rate of 82.8-100.2 % was achieved for C16:0, C18:0, C18:1, C18:2, and C18:3. The correlation of C16:0, C18:1, and C18:2 in domestic sesame oils showed opposite trends compared to imported oils. The PCA plot demonstrated that sesame oils were clustered in distinct groups according to their origin. LDA was used to predict sesame oil samples in one of the two groups. C16:0 (Wilks λ = 0.361) and C18:1 (Wilks λ = 0.637) demonstrated the highest discriminant power for classifying the origin of the samples. The correct prediction rates were 88.9 % and 100 % for the domestic and imported samples, respectively. Further, 60 of the 62 sesame oil samples (96.8 %) were correctly classified, indicating that this approach can be used as a valuable tool to predict and classify the geographical origin of sesame oils.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.1111-1116
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• 2009

In this paper, we discuss distortion-tolerant pattern recognition using computational integral imaging reconstruction. Three-dimensional object information is captured by the integral imaging pick-up process. The captured information is numerically reconstructed at arbitrary depth-levels by averaging the corresponding pixels. We apply Fisher linear discriminant analysis combined with principal component analysis to computationally reconstructed images for the distortion-tolerant recognition. Fisher linear discriminant analysis maximizes the discrimination capability between classes and principal component analysis reduces the dimensionality with the minimum mean squared errors between the original and the restored images. The presented methods provide the promising results for the classification of out-of-plane rotated objects.