• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.229-232
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• 2005

컴퓨터 비전을 기반으로 하는 인간과 컴퓨터의 상호작용(Human computer Interaction, HCI)에 대한 연구는 영상처리 분야에서 큰 축을 담당하고 있으며, 특히 얼굴인식 연구는 HCI 분야에서 가장 중요한 영역들 중의 분야이다. 이러한 얼굴인식 기반의 HCI 시스템을 구현하기 위해서는 영상 내에 존재하는 얼굴을 정확히 검증하는 것이 선행되어야 한다. 본 논문에서는 피부색상과 원형 투영 과정에 의한 특징 추출을 이용한 특징점 기반의 얼굴 검출 기법을 제안한다. 본 논문에서 제안하는 얼굴검출 기법은 얼굴의 크기 및 평면적 회전(rotation)에 대하여 강인한 얼굴검출 성능을 보여준다.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.656-659
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• 2016

최근 컴퓨터 기술의 발전과 함께 임베디드 기기 또한 다양한 기능을 갖추기 시작했다. 본 연구에서는 최근 활발하게 진행되고 있는 영상센서를 사용한 임베디드 기기 등 자원이 적은 기기에서 효율적인 얼굴 추적 방식을 제안한다. 정확한 얼굴을 얻기 위하여 MB-LBP 특징을 사용한 얼굴 검출 방식을 사용했으며, 다음 영상에서 얼굴 객체 추적을 위하여 얼굴 검출시 얼굴 주변 영역(Region of Interest)을 지정하였다. 그리고 얼굴을 검출을 못하는 영상에서는 기존의 객체 추적 방식인 CAM-Shift를 사용해 객체를 추적해 객체 정보의 손실 없이 정보를 유지 할 수 있도록 하였다. 본 연구는 기존 연구와의 비교를 통하여 객체 추적 시스템의 정확성과 빠른 성능을 확인하였다.

• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1247-1259
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• 2005

This paper proposes a real-time face recognition system which detects multiple faces from low resolution video such as web-camera video. Face recognition system consists of the face detection step and the face classification step. At First, it finds face region candidates by using AdaBoost based object detection method which have fast speed and robust performance. It generates reduced feature vector for each face region candidate by using principle component analysis. At Second, Face classification used Principle Component Analysis and multi-SVM. Experimental result shows that the proposed method achieves real-time face detection and face recognition from low resolution video. Additionally, We implement the auto-tracking face recognition system using the Pan-Tilt Web-camera and radio On/Off digital door-lock system with face recognition system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.9
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• pp.1972-1978
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• 2010

AAM(Active Appearance Model) is one of the most effective ways to detect deformable 2D objects and is a kind of mathematical optimization methods. The cost function is a convex function because it is a least-square function, but the search space is not convex space so it is not guaranteed that a local minimum is the optimal solution. That is, if the initial value does not depart from around the global minimum, it converges to a local minimum, so it is difficult to detect face contour correctly. In this study, an AAM-based face tracking algorithm is proposed, which is robust to various lighting conditions and backgrounds. Eye detection is performed using SIFT and Genetic algorithm, the information of eye are used for AAM's initial matching information. Through experiments, it is verified that the proposed AAM-based face tracking method is more robust with respect to pose and background of face than the conventional basic AAM-based face tracking method.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.809-817
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• 2002

This paper describes a new face detection method that is a pre-processing algorithm for on-line face recognition. To complement the weakness of using only edge or rotor features from previous face detection method, we propose the two types of face detection method. The one is a combined method with edge and color features and the other is a center area color sampling method. To prevent connecting the people's face area and the background area, which have same colors, we propose a new adaptive edge detection algorithm firstly. The adaptive edge detection algorithm is robust to illumination variance so that it extracts lots of edges and breakouts edges steadily in border between background and face areas. Because of strong edge detection, face area appears one or multi regions. We can merge these isolated regions using color information and get the final face area as a MBR (Minimum Bounding Rectangle) form. If the size of final face area is under or upper threshold, color sampling method in center area from input image is used to detect new face area. To evaluate the proposed method, we have experimented with 2,100 face images. A high face detection rate of 96.3% has been obtained.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.783-788
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• 2004

Many reported methods assume that the faces in an image or an image sequence have been identified and localization. Face detection from image is a challenging task because of the variability in scale, location, orientation and pose. The difficulties in visual detection and recognition are caused by the variations in viewpoint, viewing distance, illumination. In this paper, we present an efficient linear discriminant for multi-view face detection and face location. We define the training data by using the Fisher`s linear discriminant in an efficient learning method. Face detection is very difficult because it is influenced by the poses of the human face and changes in illumination. This idea can solve the multi-view and scale face detection problems. In this paper, we extract the face using the Fisher`s linear discriminant that has hierarchical models invariant size and background. The purpose of this paper is to classify face and non-face for efficient Fisher`s linear discriminant.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.488-491
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• 2003

본 논문에서는 실시간 CCD 카메라 입력 영상으로부터 다중 컬러 정보를 이용하여 얼굴 영역을 검출 및 추적하고 기울어진 얼굴을 보정하는 알고리즘을 제안하였다. 제안한 알고리즘은 먼저 획득된 RGB 영상에서 YCbCr컬러 모델과 YIQ컬러 모델로 변환한 후 Cr성분과 I성분을 추출하여 얼굴 피부색을 검출, 얼굴 영역 추출에 사용하였다. 또한 추출된 얼굴 후보 영역에서 수평, 수직 투영(Projection)정보로부터 최종 얼굴 영역으로 검출한 다음 검출된 얼굴 중심 좌표와 이전에 검출된 얼굴 중심 좌표 값을 유클리드언 거리로 얼굴을 추적하였으며 검출된 얼굴로부터 레이블링(Labeling)기법으로 눈 특징자를 검출, 눈의 기울기 각도를 보정함으로써 얼굴 기울기를 보정하였다. 제안한 얼굴 추적 및 기울기 보정 알고리즘을 사용하여 실험한 결과 다중 색상 정보를 사용함으로써 주위환경 변화에 강인하게 실시간 얼굴 영역 김출 및 추적이 가능하였고, 기울어진 얼굴 영상을 자동 보정함으로써 인식에 용이하였다.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.1
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• pp.9-14
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• 2017

Embedded devices with the development of modern computer technology also began equipped with a variety of functions. In this study, to provide a method of tracking efficient face with a small instrument of resources, such as built-in equipment that uses an image sensor in recent years has been actively carried out. It uses a face detection method using the features of the MB-LBP in order to obtain an accurate face, specify the region (Region of Interest) around the face when the face detection for the face object tracking in the next video did. And in the video can not be detected faces, to track objects using the CAM-Shift key is a conventional object tracking method, which make it possible to retain the information without loss of object information. In this study, through the comparison with the previous studies, it was confirmed the precision and high-speed performance of the object tracking system.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.1-10
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• 2007

In this paper, we propose a face recognition system using AAM and Gabor feature vectors. EBGM, which is prominent among face recognition algorithms employing Gabor feature vectors, requires localization of facial feature points where Gabor feature vectors are extracted. However, localization of facial feature points employed in EBGM is based on Gator jet similarity and is sensitive to initial points. Wrong localization of facial feature points affects face recognition rate. AAM is known to be successfully applied to localization of facial feature points. In this paper, we propose a facial feature point localization method which first roughly estimate facial feature points using AAM and refine facial feature points using Gabor jet similarity-based localization method with initial points set by the facial feature points estimated from AAM, and propose a face recognition system based on the proposed localization method. It is verified through experiments that the proposed face recognition system using the combined localization performs better than the conventional face recognition system using the Gabor similarity-based localization only like EBGM.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.2
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• pp.33-44
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• 2010

This paper propose the structure of real-time face detection hardware architecture for robot vision processing applications. The proposed architecture is robust against illumination changes and operates at no less than 60 frames per second. It uses Modified Census Transform to obtain face characteristics robust against illumination changes. And the AdaBoost algorithm is adopted to learn and generate the characteristics of the face data, and finally detected the face using this data. This paper describes the face detection hardware structure composed of Memory Interface, Image Scaler, MCT Generator, Candidate Detector, Confidence Comparator, Position Resizer, Data Grouper, and Detected Result Display, and verification Result of Hardware Implementation with using Virtex5 LX330 FPGA of Xilinx. Verification result with using the images from a camera showed that maximum 32 faces per one frame can be detected at the speed of maximum 149 frame per second.