• 한국산업융합학회 논문집
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• 제8권3호
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• pp.143-148
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• 2005

This paper presents a hybrid method for recognizing the faces by using principal component analysis(PCA) and fixed-point independent component analysis(FP-ICA). PCA is used to whiten the data, which reduces the effects of second-order statistics to the nonlinearities. FP-ICA is applied to extract the statistically independent features of face image. The proposed method has been applied to the problems for recognizing the 20 face images(10 persons * 2 scenes) of 324*243 pixels from Yale face database. The 3 distances such as city-block, Euclidean, negative angle are used as measures when match the probe images to the nearest gallery images. The experimental results show that the proposed method has a superior recognition performances(speed, rate). The negative angle has been relatively achieved more an accurate similarity than city-block or Euclidean.

• Journal of Information Processing Systems
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• 제8권3호
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• pp.483-494
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• 2012

Many recent events, such as terrorist attacks, exposed defects in most sophisticated security systems. Therefore, it is necessary to improve security data systems based on the body or behavioral characteristics, often called biometrics. Together with the growing interest in the development of human and computer interface and biometric identification, human face recognition has become an active research area. Face recognition appears to offer several advantages over other biometric methods. Nowadays, Principal Component Analysis (PCA) has been widely adopted for the face recognition algorithm. Yet still, PCA has limitations such as poor discriminatory power and large computational load. This paper proposes a novel algorithm for face recognition using a mid band frequency component of partial information which is used for PCA representation. Because the human face has even symmetry, half of a face is sufficient for face recognition. This partial information saves storage and computation time. In comparison with the traditional use of PCA, the proposed method gives better recognition accuracy and discriminatory power. Furthermore, the proposed method reduces the computational load and storage significantly.

• 전자공학회논문지CI
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• 제41권5호
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• pp.33-40
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• 2004

본 논문에서는 웨이블릿 압축을 이용하여 얼굴 데이터베이스를 구축하고, 주성분 분석(Principal Component Analysis : PCA) 알고리듬을 이용하여 얼굴 인식률을 비교한다. 일반적인 얼굴인식 방법은 정규화된 크기를 이용하여 데이터베이스를 구축하고, 얼굴 인식을 한다. 제안된 방법은 정규화된 크기(92×112)의 영상을 웨이블릿 압축으로 1단계, 2단계, 3단계로 변환하고 데이터베이스를 구축한다. 입력 영상도 웨이블릿으로 압축하고 PCA 알고리듬으로 얼굴인식 실험을 하였다 실험을 통하여 제안된 방법은 기존 얼굴영상의 정보를 축소할 뿐만 아니라 처리속도도 향상되었다. 또한 제안된 방법은 원본 영상이 99.05%, 1단계 99.05%, 2단계 98.93%, 3단계 98.54% 정도의 인식률을 보였으며, 대량의 얼굴 데이터베이스를 구축하여 얼굴인식을 하는데 가능함을 보였다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 추계학술대회 학술발표 논문집 제16권 제2호
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• pp.305-309
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• 2006

In this paper, a face recognition algorithm system using Principle Component Analysis is proposed. The algorithm recognized a person by comparing characteristics (features) of the face to those of known individuals which is a face database of Intelligence Control Laboratory(ICONL). Experiments were simulated in order to demonstrate the performance of this algorithm due to face recognition which presented for the classification of face and non-face and the classification of known and unknown.

• 전기학회논문지
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• 제58권12호
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• pp.2511-2519
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• 2009

In this paper, we introduce the methodological system design via feature selection using Principal Component Analysis and Particle Swarm Optimization algorithms. The overall methodological system design comes from three kinds of modules such as preprocessing module, feature extraction module, and recognition module. First, Histogram equalization enhance the quality of image by exploiting contrast effect based on the normalized function generated from histogram distribution values of 2D face image. Secondly, PCA extracts feature vectors to be used for face recognition by using eigenvalues and eigenvectors obtained from covariance matrix. Finally the feature selection for face recognition among the entire feature vectors is considered by means of the Particle Swarm Optimization. The optimized Polynomial-based Radial Basis Function Neural Networks are used to evaluate the face recognition performance. This study shows that the proposed methodological system design is effective to the analysis of preferred face recognition.

• Steel and Composite Structures
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• 제38권6호
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• pp.637-656
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• 2021

The component-based method is widely used to analyze the initial stiffness of joint in steel structures. In this study, an analytical component model for determining the column face stiffness of square or rectangular hollow section (SHS/RHS) subjected to tension was established, focusing on endplate connections. Equations for calculating the stiffness of the SHS/RHS column face in bending were derived through regression analysis using numerical results obtained from a finite element model database. Because the presence of bolt holes decreased the bending stiffness of the column face, this effect was calculated using a novel plate-spring-based model through numerical analysis. The developed component model was first applied to predict the bending stiffness of the SHS column face determined through tests. Furthermore, this model was incorporated into the component-based method with other effective components, e.g., bolts under tension, to determine the tensile stiffness of the T-stub connections, which connects the SHS column, and the initial rotational stiffness of the joints. A comparison between the model predictions, test data, and numerical results confirms that the proposed model shows satisfactory accuracy in evaluating the bending stiffness of SHS column faces.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.185-189
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• 2004

For face recognition system, a face detector which can find exact face region from complex image is needed. Many face detection algorithms have been developed under the assumption that background of the source image is quite simple . this means that face region occupy more than a quarter of the area of the source image or the background is one-colored. Color-based face detection is fast but can't be applicable to the images of which the background color is similar to face color. And the algorithm using neural network needs so many non-face data for training and doesn't guarantee general performance. In this paper, A multi-scale, multi-face detection algorithm using PCA is suggested. This algorithm can find most multi-scaled faces contained in static images with small number of training data in reasonable time.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -2
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• pp.873-876
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• 2000

The information-theoretic approach to face recognition is based on the compact coding where face images are decomposed into a small set of basis images. Most popular method for the compact coding may be the principal component analysis (PCA) which eigenface methods are based on. PCA based methods exploit only second-order statistical structure of the data, so higher- order statistical dependencies among pixels are not considered. Independent component analysis (ICA) is a signal processing technique whose goal is to express a set of random variables as linear combinations of statistically independent component variables. ICA exploits high-order statistical structure of the data that contains important information. In this paper we employ the ICA for the efficient feature extraction from face images and show that ICA outperforms the PCA in the task of face recognition. Experimental results using a simple nearest classifier and multi layer perceptron (MLP) are presented to illustrate the performance of the proposed method.

• Journal of Multimedia Information System
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• 제2권1호
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• pp.171-178
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• 2015

This article describes 3 dimensional (3D) face recognition system using histogram of oriented gradients (HOG) based on face curvature. The surface curvatures in the face contain the most important personal feature information. In this paper, 3D face images are recognized by the face components: cheek, eyes, mouth, and nose. For the proposed approach, the first step uses the face curvatures which present the facial features for 3D face images, after normalization using the singular value decomposition (SVD). Fisherface method is then applied to each component curvature face. The reason for adapting the Fisherface method maintains the surface attribute for the face curvature, even though it can generate reduced image dimension. And histogram of oriented gradients (HOG) descriptor is one of the state-of-art methods which have been shown to significantly outperform the existing feature set for several objects detection and recognition. In the last step, the linear discriminant analysis is explained for each component. The experimental results showed that the proposed approach leads to higher detection accuracy rate than other methods.

• 전자공학회논문지CI
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• 제41권2호
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• pp.131-136
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• 2004

미디어의 발달에 따라 다양한 정보가 미디어에 실리게 되는데 이중에서 표정은 흥미있는 정보중에 하나이다. 표정에는 인간 내면의 의중이 포함되어 있기 때문이다. 내면의 상태는 표정 뿐만 아니라 제스처로 나타나기도 하지만 중요도는 표정이 높다고 할 수 있다. 이 표정은 임의로 조작할 수도 있지만 대체로 내면의 의중을 포함한다. 또한 표정은 사람마다 독특한 개성을 가지고 있지만 대체로 일정 구분 가능한 공통점 또한 가지고 있다. 본 논문에서는 USB CCD 카메라 환경의 동영상에서 표정을 분석하기 위해서 얼굴의 구성 요소를 검출 하고자 한다. 얼굴 구성요소에는 사람마다의 공통적인 표정 변화에 따른 특징점이 분포하기 때문이다. 구성 요소 검출을 위해서 먼저 동영상에서 한 프레임을 캡처하여 얼굴의 위치를 파악하고 얼굴영역을 분리한 다음 특징 점을 검출하게 된다.