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

• Journal of Korea Multimedia Society
• /
• v.9 no.4
• /
• pp.470-481
• /
• 2006

In this paper, we propose a real-time face detection, tracking and tilted face image correction system using multi-color model and face feature information. In the proposed system, we detect face candidate using YCbCr and YIQ color model. And also, we detect face using vertical and horizontal projection method and track people's face using Hausdorff matching method. And also, we correct tilted face with the correction of tilted eye features. The experiments have been performed for 110 test images and shows good performance. Experimental results show that the proposed algorithm robust to detection and tracking of face at real-time with the change of exterior condition and recognition of tilted face. Accordingly face detection and tilted face correction rate displayed 92.27% and 92.70% respectively and proposed algorithm shows 90.0% successive recognition rate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• v.9 no.1
• /
• pp.828-831
• /
• 2005

In this paper we propose a method to detect human faces from color image using pupil-template matching. Face detection is done by three stages. (i)separating skin regions from non-skin regions; (ii)generating a face regions by application of the best-fit ellipse; (iii)detecting face by pupil-template. Detecting skin regions is based on a skin color model. we generate a gray scale image from original image by the skin model. The gray scale image is segmented to separated skin regions from non-skin regions. Face region is generated by application of the best-fit ellipse is computed on the base of moments. Generated face regions are matched by pupil-template. And we detection face.

• Proceedings of the Korean Information Science Society Conference
• /
• 2012.06c
• /
• pp.329-331
• /
• 2012

본 논문에서는 영상에서의 시각적 자극의 특징에 의한 돌출과 특정 대상에 관련한 단서들간의 상호작용에 기반하여 얼굴을 검출하는 주의집중모델을 제안한다. 제안하는 모델은 얼굴에 대한 하향식 다중 단서로 모양(shape), 피부색(skin color), 밝기(luminance), 거리에 대응하는 크기, 깊이 등을 사용하며 이들 단서들이 상향식 프로세스와의 상호작용을 통해 목표하는 얼굴을 검출하도록 유도하는 상향식/하향식 결합에 기반한다. 제안하는 방법은 크기 및 회전변화를 갖는 다수의 얼굴을 포함한 영상에서 얼굴검출을 수행함으로써 성능을 검증하였다.

• Proceedings of the KIEE Conference
• /
• 2006.07d
• /
• pp.2111-2112
• /
• 2006

얼굴 영상에서 사람의 눈을 검출하는 것은 얼굴 인식의 전체적인 성능을 좌우하는 매우 중요한 사항이다. 눈 검출은 얼굴 영상의 특징이 변하기 때문에 항상 신뢰할 수 있는 결과를 얻는 것은 어려우며, 또한 실시간 얼굴 인식에 응용되기 위해서는 빠른 연산 시간도 고려되어야 한다. 본 논문에서는 빠르고 정확한 새로운 눈 검출 방법을 제안하다. 첫째, Ada-Boosting 알고리즘을 사용하여 얼굴 영역을 검출한다. 둘째, Intensity valley와 edge 정보를 사용하여 얼굴 영상의 회전(Rotation in plane)을 보상한다. 셋째, Intensity edge정보를 사용하여 두 눈의 수직, 수평라인을 검출한다. 넷째, 일반적인 (generic) 사람 눈의 패턴을 이용하여 고안된 Filter로 두 눈의 위치를 검출한다. 본 논문을 통하여 새로 제안된 알고리즘에 대한 논의와 실험 결과를 통해 새로운 알고리즘이 눈 검출에 적합함을 제시한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2006.11a
• /
• pp.131-134
• /
• 2006

본 논문에서는 가정용 로봇 및 서비스 로봇과 같은 이동로봇에서 사용자의 감정을 인식하는 방법중 한가지인 얼굴영상을 이용한 감정인식 방법을 제안한다. 얼굴영상인식을 위하여 얼굴의 여러 가지 특징(눈썹, 눈, 코, 입)의 움직임 및 위치를 이용하며, 이동로봇에서 움직이는 사용자를 인식하기 위한 움직임 추적 알고리즘을 구현하고, 획득된 사용자의 영상에서 얼굴영역 검출 알고리즘을 사용하여 얼굴 영역을 제외한 손과 배경 영상의 피부색은 제거한다. 검출된 얼굴영역의 거리에 따른 영상 확대 및 축소, 얼굴 각도에 따른 영상 회전변환 등의 정규화 작업을 거친 후 이동 로봇에서는 항상 고정된 크기의 얼굴 영상을 획득 할 수 있도록 한다. 또한 기존의 특징점 추출이나 히스토그램을 이용한 감정인식 방법을 혼합하여 인간의 감성 인식 시스템을 모방한 로봇에서의 감정인식을 수행한다. 본 논문에서는 이러한 다중 특징점 추출 방식을 통하여 이동로봇에서의 얼굴 영상을 이용한 사용자의 감정인식 시스템을 제안한다.

• Journal of KIISE:Software and Applications
• /
• v.30 no.7_8
• /
• pp.773-779
• /
• 2003

Locating exact position of facial components is a key preprocessing for realizing highly accurate and reliable face recognition schemes. In this paper, we propose a simple but powerful method for detecting isolated facial components such as eyebrows, eyes, and a mouth, which are horizontally oriented and have relatively dark gray levels. The method is based on the shape-resolving locally optimum thresholding that may guarantee isolated detection of each component. We show that pure facial regions can be determined by grouping facial features satisfying simple geometric constraints on unique facial structure. In the test for over 1000 images in the AR -face database, pure facial regions were detected correctly for each face image without wearing glasses. Very few errors occurred in the face images wearing glasses with a thick frame because of the occluded eyebrow -pairs. The proposed scheme may be best suited for the later stage of classification using either the mappings or a template matching, because of its capability of handling rotational and translational variations.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.2 no.3
• /
• pp.53-60
• /
• 2009

The vision-based driver fatigue detection is one of the most prospective commercial applications of facial expression recognition technology. The facial feature tracking is the primary technique issue in it. Current facial tracking technology faces three challenges: (1) detection failure of some or all of features due to a variety of lighting conditions and head motions; (2) multiple and non-rigid object tracking; and (3) features occlusion when the head is in oblique angles. In this paper, we propose a new active approach. First, the active IR sensor is used to robustly detect pupils under variable lighting conditions. The detected pupils are then used to predict the head motion. Furthermore, face movement is assumed to be locally smooth so that a facial feature can be tracked with a Kalman filter. The simultaneous use of the pupil constraint and the Kalman filtering greatly increases the prediction accuracy for each feature position. Feature detection is accomplished in the Gabor space with respect to the vicinity of predicted location. Local graphs consisting of identified features are extracted and used to capture the spatial relationship among detected features. Finally, a graph-based reliability propagation is proposed to tackle the occlusion problem and verify the tracking results. The experimental results show validity of our active approach to real-life facial tracking under variable lighting conditions, head orientations, and facial expressions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2004.05b
• /
• pp.603-607
• /
• 2004

The vision-based driver fatigue detection is one of the most prospective commercial applications of facial expression recognition technology. The facial feature tracking is the primary technique issue in it. Current facial tracking technology faces three challenges: (1) detection failure of some or all of features due to a variety of lighting conditions and head motions; (2) multiple and non-rigid object tracking and (3) features occlusion when the head is in oblique angles. In this paper, we propose a new active approach. First, the active IR sensor is used to robustly detect pupils under variable lighting conditions. The detected pupils are then used to predict the head motion. Furthermore, face movement is assumed to be locally smooth so that a facial feature can be tracked with a Kalman filter. The simultaneous use of the pupil constraint and the Kalman filtering greatly increases the prediction accuracy for each feature position. Feature detection is accomplished in the Gabor space with respect to the vicinity of predicted location. Local graphs consisting of identified features are extracted and used to capture the spatial relationship among detected features. Finally, a graph-based reliability propagation is proposed to tackle the occlusion problem and verify the tracking results. The experimental results show validity of our active approach to real-life facial tracking under variable lighting conditions, head orientations, and facial expressions.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.6
• /
• pp.109-116
• /
• 2019

This study proposed a robust detection algorithm. It detects face more stably with respect to changes in light and rotation for the identification of a face shape. Also it satisfies both efficiency of calculation and the function of detection. The algorithm proposed segmented the face area through pre-processing using a face shape as input information in an environment with a single camera and then identified the shape using a Self Organized Feature Map(SOFM). However, as it is not easy to exactly recognize a face area which is sensitive to light, it has a large degree of freedom, and there is a large error bound, to enhance the identification rate, rotation information on the face shape was made into a database and then a principal component analysis was conducted. Also, as there were fewer calculations due to the fewer dimensions, the time for real-time identification could be decreased.