• 한국방사선학회논문지
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• 제10권8호
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• pp.571-576
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• 2016

3D 프린팅은 최근 다양한 분야에서 활용 되고 있다. 다양한 활용 분야 중 사람의 얼굴을 3D 프린팅을 위해서는 먼저 3D 얼굴 데이터를 생성해야 한다. 3D 얼굴 데이터 획득을 위해 레이저 스캐너 등이 활용되고 있으나 스캔 중에 사람이 움직이면 안 되는 제약이 있다. 본 논문에서는 단일 영상 기반의 3D 얼굴 모델링 방법과 생성된 3D 얼굴을 가상 성형 등에 쓰일 수 있도록 얼굴 변형 시스템을 제안한다. 3D 얼굴 데이터 생성을 위해 3D 얼굴 데이터베이스로부터 특징점들을 정의하였다. 단일 얼굴 영상으로부터 얼굴을 특징점을 추출 한 후 3D 얼굴 데이터베이스로부터 정의된 3D 얼굴 특징점과 대응하여 입력 얼굴 영상의 3D 얼굴을 생성한다. 3D 얼굴 생성 후에 가상 성형 등의 용도를 위해 얼굴 변형 부분을 적용하였다.

• 한국통신학회논문지
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• 제35권12C호
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• pp.984-994
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• 2010

Pose variation is a critical problem in face recognition. Three-dimensional(3D) face recognition techniques have been proposed, as 3D data contains depth information that may allow problems of pose variation to be handled more effectively than with 2D face recognition methods. This paper proposes a pose-normalized 3D face modeling method that translates and rotates any pose angle to a frontal pose using a plane fitting method by Singular Value Decomposition(SVD). First, we reconstruct 3D face data with stereo vision method. Second, nose peak point is estimated by depth information and then the angle of pose is estimated by a facial plane fitting algorithm using four facial features. Next, using the estimated pose angle, the 3D face is translated and rotated to a frontal pose. To demonstrate the effectiveness of the proposed method, we designed 2D and 3D face recognition experiments. The experimental results show that the performance of the normalized 3D face recognition method is superior to that of an un-normalized 3D face recognition method for overcoming the problems of pose variation.

• 사상체질의학회지
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• 제19권2호
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• pp.30-39
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• 2007

1. Objectives The Face is an important standard for the classification of Sasang Constitution. We are developing 3D Automatic Face Recognition Apparatus(3D-AFRA) to analyse the facial characteristics. This apparatus show us 3D image and data of man's face and measure facial figure data. So we should examine the figure restoration error of 3D Automatic Fare Recognition Apparatus(3D-AFRA) in hardware Error Analysis. 2. Methods We scanned Face status by using 3D Automatic Face Recognition Apparatus(3D-AFRA). And also we scanned Face status by using laser scanner(vivid 9i). We compared facial shape data be restored by 3D Automatic Face Recognition Apparatus(3D-AFRA) with facial shape data that be restorated by 3D laser scanner. And we analysed the average error and the maximum error of two data. 3. Results and Conclusions In frontal face, the average error was 0.48mm. and the maximum error was 4.60mm. In whole face, the average error of was 0.99mm. And the maximum error was 6.64mm. In conclusion, We assessed that accuracy of 3D Automatic Face Recognition Apparatus(3D-AFRA) is considerably good.

• 전기학회논문지
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• 제63권6호
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• pp.797-803
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• 2014

In this paper, 3D face recognition system is designed by using polynomial based on RBFNNs. In case of 2D face recognition, the recognition performance reduced by the external environmental factors such as illumination and facial pose. In order to compensate for these shortcomings of 2D face recognition, 3D face recognition. In the preprocessing part, according to the change of each position angle the obtained 3D face image shapes are changed into front image shapes through pose compensation. the depth data of face image shape by using Multiple Point Signature is extracted. Overall face depth information is obtained by using two or more reference points. The direct use of the extracted data an high-dimensional data leads to the deterioration of learning speed as well as recognition performance. We exploit principle component analysis(PCA) algorithm to conduct the dimension reduction of high-dimensional data. Parameter optimization is carried out with the aid of PSO for effective training and recognition. The proposed pattern classifier is experimented with and evaluated by using dataset obtained in IC & CI Lab.

• 한국멀티미디어학회논문지
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• 제12권5호
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• pp.737-744
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• 2009

본 논문은 한국 표준 얼굴 데이터를 바탕으로 제작한 3D 얼굴 모델을 활용하여 성형 수술 전후의 모습을 비교하여 성형 수술의 만족도를 높이는 웅용 프로그랭인 3D 가장 얼굴 성형 시스댐에 대한 연구 및 구현이다. 이 연구를 위하여 2D 개인 얼굴 이미지와 3D 한국인 표준 모델을 활용하여, 2D 이미지 상의 사용자의 실제 얼굴과 똑같은 3D 얼굴 모델을 구현한다. 본 연구에서 제안하는 3D 가상 얼굴 성형 시스템은 얼굴 모텔의 기본이 되는 3D 한국인 표준 얼굴 모델에 2D 개인 얼굴 이미지로부터 추출한 개인 특성 정보를 적용함으로서 3D 얼굴 모델링 시스템을 구현하고 3D 얼굴의 사실성올 높이는 텍스처 맴핑 및 구축된 얼굴을 활용하여 성형 수술 후의 얼굴의 변화를 나타내는 3D 얼굴 성형 시스댐 등으로 구성되어 있다. 제안된 3D 가상 얼굴 성형 시스템은 정확한 개인 얼굴 모델링 방법을 제공하고, 성형 전후의 얼굴을 미세하게 조정하는 기능을 부여함으로써 현존하는 유사 시스템에 비교하여 성형 수술의 만족도와 안정성을 높이는 결과를 나타내었다.

• 전기학회논문지
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• 제64권6호
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• pp.900-911
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• 2015

In this paper, 3D face recognition model is designed by using Polynomial based RBFNN(Radial Basis Function Neural Network) and PNN(Polynomial Neural Network). Also recognition rate is performed by this model. In existing 2D face recognition model, the degradation of recognition rate may occur in external environments such as face features using a brightness of the video. So 3D face recognition is performed by using 3D scanner for improving disadvantage of 2D face recognition. In the preprocessing part, obtained 3D face images for the variation of each pose are changed as front image by using pose compensation. The depth data of face image shape is extracted by using Multiple point signature. And whole area of face depth information is obtained by using the tip of a nose as a reference point. Parameter optimization is carried out with the aid of both ABC(Artificial Bee Colony) and PSO(Particle Swarm Optimization) for effective training and recognition. Experimental data for face recognition is built up by the face images of students and researchers in IC&CI Lab of Suwon University. By using the images of 3D face extracted in IC&CI Lab. the performance of 3D face recognition is evaluated and compared according to two types of models as well as point signature method based on two kinds of depth data information.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.455-456
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• 2006

This paper presents an automatic pose-normalized 3D face data acquisition method using 2D and 3D information. We propose an automatic pose-normalized 3D face acquisition method that accomplishes 3D face modeling and 3D face pose-normalization at once. The proposed method uses 2D information with AAM (Active Appearance Model) and 3D information with 3D normal vector. The 3D face modeling system consists of 2 cameras and 1 projector. In order to verify proposed pose-normalized 3D modeling method, we made an experiment for 2.5D face recognition. The experimental result shows that proposed method is robust against pose variation.

• 정보보호학회논문지
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• 제24권4호
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• pp.607-616
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• 2014

현재 영상감시 시스템에서 얼굴 인식을 통한 사람의 신원 확인은 정면 얼굴이 아닌 관계로 매우 어려운 기술에 속한다. 일반적인 사람들의 얼굴 영상과 입력된 얼굴 영상을 비교하여 유사도를 파악하고 신원을 확인 하는 기술은 각도의 차이에 따라 정확도의 오차가 심해진다. 이런 문제를 해결하기 위해 본 논문에서는 POSIT을 사용하여 얼굴 포즈 측정을 하고, 추정된 각도를 이용하여 3D 얼굴 영상을 제작 후 매칭 하여 일반적인 정면 영상끼리의 매칭이 아닌 rotated face를 이용한 매칭을 해보기로 한다. 얼굴을 매칭 하는 데는 상용화된 얼굴인식 알고리즘을 사용하였다. 얼굴 포즈 추정은 $10^{\circ}$이내의 오차를 보였고, 얼굴인증 성능은 약 95% 정도임을 확인하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.929-930
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• 2008

To develop reliable 3D face recognition system, many researchers also have focused on 3D face data acquisition system. Previous many 3D face acquisition systems use visible patterns to solve corresponding problem, and this pattern made anyone who wants to be verified uncomfortable. In this paper, we propose a new invisible infrared line-laser pattern for 3D face data acquisition.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권2호
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• pp.84-90
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• 2014

This paper presents the results of three-dimensional face point cloud smoothing based on a modified anisotropic diffusion method. The focus of this research was to obtain a 3D face point cloud with a smooth texture and number of vertices equal to the number of vertices input during the smoothing process. Different from other methods, such as using a template D face model, modified anisotropic diffusion only uses basic concepts of convolution and filtering which do not require a complex process. In this research, we used 6D point cloud face data where the first 3D point cloud contained data pertaining to noisy x-, y-, and z-coordinate information, and the other 3D point cloud contained data regarding the red, green, and blue pixel layers as an input system. We used vertex selection to modify the original anisotropic diffusion. The results show that our method has improved performance relative to the original anisotropic diffusion method.