• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2024-2027
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• 2003

본 논문에서는 얼굴 인식 분야에서 사용되는 PCA/LDA 알고리즘을 대신하기 위해 DCT/LDA 알고리즘을 제안하였다. PCA/LDA를 이용한 얼굴 인식의 경우 PCA 를 이용하여 얼굴 영상을 적은 수의 특징 값으로 표현한 다음 LDA를 수행한다. 그러나 PCA는 트레이닝 과정의 계산량이 많고 트레이닝 셋이 변할 때마다 기저 벡터가 변화한다. PCA/LDA의 단점을 개선하기 위해 계산량이 적고 기저 벡터가 일정한 DCT의 계수를 사용한다. DCT/LDA를 사용할 경우 특징 값을 빠르게 추출하면서 PCP/LDA와 유사한 성능을 얻을 수 있다. 실험을 통하여 포즈 변화와 조명 변화가 있는 얼굴 데이터 셋에서 최고 97.8%의 인식률을 보였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.167-170
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• 2006

본 논문은 선형분류기인 LDA 융합모델과 최소거리패턴분류법을 이용한 얼굴표정인식 알고리즘 연구에 관한 것이다. 제안된 알고리즘은 얼굴 표정을 인식하기 위해 두 단계의 특징 추출과정과 인식단계를 거치게 된다. 먼저 특징추출 단계에서는 얼굴 표정이 담긴 영상을 PCA를 이용해 고차원에서 저차원의 공간으로 변환한 후, LDA 이용해 특징벡터를 클래스 별로 나누어 분류한다. 다음 단계로 LDA융합모델을 통해 계산된 특징벡터에 최소거리패턴분류법을 적용함으로서 얼굴 표정을 인식한다. 제안된 알고리즘은 6가지 기본 감정(기쁨, 화남, 놀람, 공포, 슬픔, 혐오)으로 구성된 데이터베이스를 이용해 실험한 결과, 기존알고리즘에 비해 향상된 인식률과 특정 표정에 관계없이 고른 인식률을 보임을 확인하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.759-765
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• 2006

In this paper, it is proposed an effective face expression recognition LDA mixed mode using a triangularity membership fuzzy function and wavelet basis. The proposal algorithm gets performs the optimal image, fuzzy wavelet algorithm and Expression recognition is consisted of face characteristic detection step and face Expression recognition step. This paper could applied to the PCA and LDA in using some simple strategies and also compares and analyzes the performance of the LDA mixed model which is combined and the facial expression recognition based on PCA and LDA. The LDA mixed model is represented by the PCA and the LDA approaches. And then we calculate the distance of vectors dPCA, dLDA from all fates in the database. Last, the two vectors are combined according to a given combination rule and the final decision is made by NNPC. In a result, we could showed the superior the LDA mixed model can be than the conventional algorithm.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.385-386
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• 2020

In this paper, we propose a collaborative filtering recommendation algorithm based on the LDA2Vec topic model. By extracting and analyzing the article's content, calculate their semantic similarity then combine the traditional collaborative filtering algorithm to recommend. This approach may promote the system's recommend accuracy.

• 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 Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.2
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• pp.152-159
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• 2005

In this paper, we propose a diagnosis algorithm using fusion wかd based on PCA and LDA to detect fault states of the induction motor that is applied to various industrial fields. After yielding a feature vector from the current value measured by an experiment using PCA and LDA, training data is made to produce each matching value. In a diagnostic step, two matching values yielded by PCA and LDA are fused by probability model and finally verified. Since the proposed diagnosis algorithm takes only merits of PCA and LDA it shows excellent results under noisy environments. The simulation results to verify the usability of the proposed algorithm showed better performance than the case just using conventional PCA or LDA.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.45-50
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• 2004

In this paper, we propose a new PCA-based LDA Mixture Algorithm(PLMA) for real-time face recognition system. This system greatly consists of the two parts: 1) face extraction part; 2) face recognition part. In the face extraction part we applied subtraction image, color filtering, eyes and mouth region detection, and normalization method, and in the face recognition part we used the method mixing PCA and LDA in extracted face candidate region images. The existing recognition system using only PCA showed low recognition rates, and it is hard in the recognition system using only LDA to apply LDA to the input images as it is when the number of image pixels ire small as compared with the training set. To overcome these shortcomings, we reduced dimension as we apply PCA to the normalized images, and apply LDA to the compressed images, therefore it is possible for us to do real-time recognition, and we are also capable of improving recognition rates. We have experimented using self-organized DAUface database to evaluate the performance of the proposed system. The experimental results show that the proposed method outperform PCA, LDA and ICA method within the framework of recognition accuracy.

• 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.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.35-42
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• 2014

It is possible to improve the obstacle avoidance capability by training and recognizing the obstacles which is in certain indoor environment. We propose the technique that use underlying intensity value along with intensity map from RGB-D image which is derived from stereo vision Kinect sensor and recognize an obstacle within constant distance. We test and experiment the accuracy and execution time of the pattern recognition algorithms like PCA, ICA, LDA, SVM to show the recognition possibility of it. From the comparison experiment between RGB-D data and intensity data, RGB-D data got 4.2% better accuracy rate than intensity data but intensity data got 29% and 31% faster than RGB-D in terms of training time and intensity data got 70% and 33% faster than RGB-D in terms of testing time for LDA and SVM, respectively. So, LDA, SVM have good accuracy and better training/testing time to use for obstacle avoidance based on intensity dataset of mobile robot.