• Journal of KIISE:Computing Practices and Letters
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• v.15 no.9
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• pp.705-709
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• 2009

In this paper, we propose 3d face modeling using two orthogonal views of 2D face images and automatically facial feature extraction. Th proposed technique consists of 2 parts, personalization of 3d face model and texture mapping.

• Journal of the Korea Computer Graphics Society
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• v.8 no.4
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• pp.17-25
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• 2002

In this paper we propose a new efficient muscle modeling for creating 3d facial models. It improves the existing muscle-based facial modeling method. We present the facial muscle action prediction map as a new method of muscle modeling. We also suggest a new face deformation method that improves the existing method which controls each muscle's condition separately.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.500-506
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• 2007

얼굴에 대한 시각적 인지는 오랫동안 인간에게 중요한 문제로 인식되어 왔다. 수 세기 동안 이루어져 왔던 미용 화장과 성형, 치아 교정 등의 다양한 연구는 사람의 얼굴을 감성적 측면에서 어떻게 하면 아름답게 만들 수 있는 가에 초점을 두었다. 본 논문에서는 휴먼 입출력 인터페이스로서 햅틱 장치와 스테레오 디스플레이를 혼합한 다중 감각 인터랙션 기반의 감성적인 3차원 얼굴 메이크업 시뮬레이션 프레임워크를 개발한다. 본 연구는 3차원 스캐너 장비로부터 사용자의 얼굴 모델을 추출하고, 그 데이터를 이용하여 자연스럽고 직관적인 얼굴 메이크업 시뮬레이션을 수행하는 것을 목표로 하고 있다. 이를 위하여 본 연구에서는 surface elements 표현 기반의 3차원 얼굴 필터링 방법과 얼굴 메이크업을 지원하는 페인팅 방법을 개발한다. 우선 사용자의 얼굴 모델을 3차원 스캐너로 획득한 후, 전처리 얼굴 필터링을 수행하여 조명, 그리고 사용자 얼굴 피부 상태에 기인하는 에러 및 속성들을 보정하고 피부 톤을 사용자가 선호하는 색으로 변경한다. 최종적으로 사용자는 햅틱 및 스테레오 디스플레이 장치를 이용하여 두 개의 레이어로 구성된 페인팅 표면 모델에 메이크업을 수행한다. 본 연구에서 적용한 surface elements 표현 기반의 그래픽 렌더링은 일반적인 메쉬 기반 페인팅의 문제점인 텍스쳐 왜곡 현상을 완화하고, 3차원 스캐너 장치에 기인하는 표면 에러를 보정한다. 그리고 전처리 얼굴 필터링과 메이크업 페인팅 방법은 사용자 중심의 감성적인 3차원 얼굴을 재구성하도록 한다. 결과적으로 본 연구에서 개발한 이러한 기술들이 다중 감각 인터페이스 기반의 메이크업 시뮬레이터의 기본 프레임워크가 되어, 차후 메이크업이나 코디네이션 분야 등의 디지털 콘텐츠 산업에서 활용될 수 있음을 확인하였다.

• Journal of Advanced Navigation Technology
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• v.24 no.5
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• pp.430-437
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• 2020

The proposed model implements a model that improves the face prediction rate and recognition rate through learning with an artificial neural network using face detection, landmark and face recognition algorithms. After landmarking in the face images of a specific person, the proposed model use the previously learned Caffe model to extract face detection and embedding vector 128D. The learning is learned by building machine learning algorithms such as support vector machine (SVM) and deep neural network (DNN). Face recognition is tested with a face image different from the learned figure using the learned model. As a result of the experiment, the result of learning with DNN rather than SVM showed better prediction rate and recognition rate. However, when the hidden layer of DNN is increased, the prediction rate increases but the recognition rate decreases. This is judged as overfitting caused by a small number of objects to be recognized. As a result of learning by adding a clear face image to the proposed model, it is confirmed that the result of high prediction rate and recognition rate can be obtained. This research will be able to obtain better recognition and prediction rates through effective deep learning establishment by utilizing more face image data.

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.49-56
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• 2011

Recently, animation contents has become extensively available along with the development of cultural industry. In this paper, we propose a method to analyze a face of animation character and extract 3D rotational information of the face. The suggested method first generates a dominant color model of a face by learning the face image of animation character. Our system then detects the face and its components with the model, and establishes two coordinate systems: base coordinate system and target coordinate system. Our system estimates three dimensional rotational information of the animation character face using the geometric relationship of the two coordinate systems. Finally, in order to visually represent the extracted 3D information, a 3D face model in which the rotation information is reflected is displayed. In experiments, we show that our method can extract 3D rotation information of a character face reasonably.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.472-474
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• 2002

본 논문에서는 복잡한 영상에서 얼굴 영역의 윤곽선을 검출하는 방법을 제안하였다. 이를 위하여 얼굴의 칼라 정보에 기반한 액티브 컨투어 모델을 이용하였다. 얼굴의 칼라 정보는 색채칼라 공간(chromatic color space)에서 2D-Gaussian모델로 나타내어지는 스킨 칼로 모델로 표현 되었다. 실험결과 제안된 방법은 복잡한 영상뿐 아니라 잡음이 많은 영상에서 하나 또는 여러 개의 얼굴 영역을 추출할 수 있었다.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1373-1374
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• 2015

본 논문은 복잡한 비선형 모델링 방법인 다항식 기반 RBF 뉴럴 네트워크(Radial Basis Function Neural Network)와 벡터공간에서 임의의 비선형 경계를 찾아 두 개의 집합을 분류하는 방법으로 주어진 조건하에서 수학적으로 최적의 해를 찾는 SVM(Support Vector Machine)를 사용하여 3차원 얼굴인식 모델을 설계하고 두 모델의 3차원 얼굴 인식률을 비교한다. 3D스캐너를 통해 3차원 얼굴형상을 획득하고 획득한 영상을 전처리 과정에서 포인트 클라우드 정합과 포즈보상을 수행한다. 포즈보상 통해 정면으로 재배치한 영상을 Multiple Point Signature기법을 이용하여 얼굴의 깊이 데이터를 추출한다. 추출된 깊이 데이터를 RBFNN과 SVM의 입력패턴과 출력으로 선정하여 모델을 설계한다. 각 모델의 효율적인 학습을 위해 PCA 알고리즘을 이용하여 고차원의 패턴을 축소하여 모델을 설계하고 인식 성능을 비교 및 확인한다.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.342-347
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• 2008

Face tracking and recognition are difficult problems because the face is a non-rigid object. If the system tries to track or recognize the unknown face continuously, it can be more hard problems. In this paper, we propose the method to track and to recognize the face of the unknown person on video sequences using linear combination of nonlinear manifold models that is constructed in the system. The arbitrary input face has different similarities with different persons in system according to its shape or pose. Do we can approximate the new nonlinear manifold model for the input face by estimating the similarities with other faces statistically. The approximated model is updated at each frame for the input face. Our experimental results show that the proposed method is efficient to track and recognize for the arbitrary person.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.311-320
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• 2007

This paper presents vision-based 3D facial expression animation technique and system which provide the robust 3D head pose estimation and real-time facial expression control. Many researches of 3D face animation have been done for the facial expression control itself rather than focusing on 3D head motion tracking. However, the head motion tracking is one of critical issues to be solved for developing realistic facial animation. In this research, we developed an integrated animation system that includes 3D head motion tracking and facial expression control at the same time. The proposed system consists of three major phases: face detection, 3D head motion tracking, and facial expression control. For face detection, with the non-parametric HT skin color model and template matching, we can detect the facial region efficiently from video frame. For 3D head motion tracking, we exploit the cylindrical head model that is projected to the initial head motion template. Given an initial reference template of the face image and the corresponding head motion, the cylindrical head model is created and the foil head motion is traced based on the optical flow method. For the facial expression cloning we utilize the feature-based method, The major facial feature points are detected by the geometry of information of the face with template matching and traced by optical flow. Since the locations of varying feature points are composed of head motion and facial expression information, the animation parameters which describe the variation of the facial features are acquired from geometrically transformed frontal head pose image. Finally, the facial expression cloning is done by two fitting process. The control points of the 3D model are varied applying the animation parameters to the face model, and the non-feature points around the control points are changed by use of Radial Basis Function(RBF). From the experiment, we can prove that the developed vision-based animation system can create realistic facial animation with robust head pose estimation and facial variation from input video image.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.2
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• pp.322-327
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• 2003

Based on dynamic muscle model to construct efficient face animation in this paper 30 face modelling techniques propose. Composed face muscle by faceline that connect 256 point and this point based on dynamic muscle model, and constructed wireframe because using this. After compose standard model who use wireframe, because using front side and side 2D picture, enforce texture mapping and created 3D individual face model. Used front side of characteristic points and side part for correct mapping, after make face that have texture coordinates using 2D coordinate of front side image and front side characteristic points, constructed face that have texture coordinates using 2D coordinate of side image and side characteristic points.