• 대한한의진단학회지
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• 제17권3호
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• pp.233-240
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• 2013

Objectives The objective and universal grading system for the facial nerve palsy is needed to the objectification of treatment in Oriental medicine. In this study, the facial nerve palsy grading was developed with combination of image processing technique and Nottingham scale. Methods The developed system is composed of measurement part, image processing part, facial nerve palsy evaluation part, and display part. With the video data recorded by webcam at measurement part, the positions of marker were measured at image processing part. In evaluation part, Nottingham scales were calculated in four different facial expressions with measured marker position. The video of facial movement, time history of marker position, and Nottingham scale were displayed in display part. Results & Conclusion The developed system was applied to a normal subject and a abnormal subject with facial nerve palsy. The left-right difference of Nottingham scores was large in the abnormal compared with the normal. In normal case, the change of the length between supraorbital point and infraorbital point was larger than that of the length between lateral canthus and angle of mouth. The abnormal case showed an opposite result. The developed system showed the possibilities of the objective and universal grading system for the facial nerve palsy.

• Journal of Information Processing Systems
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• 제6권2호
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• pp.261-268
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• 2010

Tracking human facial expression within a video image has many useful applications, such as surveillance and teleconferencing, etc. Initially, the Active Appearance Model (AAM) was proposed for facial recognition; however, it turns out that the AAM has many advantages as regards continuous facial expression recognition. We have implemented a continuous facial expression recognition system using the AAM. In this study, we adopt an independent AAM using the Inverse Compositional Image Alignment method. The system was evaluated using the standard Cohn-Kanade facial expression database, the results of which show that it could have numerous potential applications.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(3)
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• pp.25-28
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• 2002

This paper introduce a personalized facial expression recognition system. Many previous works on facial expression recognition system focus on the formal six universal facial expressions. However, it is very difficult to make such expressions for normal person without much effort and training. And in these days, the personalized service is also mainly focused by many researchers in various fields. Thus, we Propose a novel facial expression recognition system with fuzzy neural networks and robust image processing.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2373-2378
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• 2005

An emotion detection algorithm using frontal facial image is presented in this paper. The algorithm is composed of three main stages: image processing stage and facial feature extraction stage, and emotion detection stage. In image processing stage, the face region and facial component is extracted by using fuzzy color filter, virtual face model, and histogram analysis method. The features for emotion detection are extracted from facial component in facial feature extraction stage. In emotion detection stage, the fuzzy classifier is adopted to recognize emotion from extracted features. It is shown by experiment results that the proposed algorithm can detect emotion well.

• Journal of Information Technology Applications and Management
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• 제10권4호
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• pp.135-147
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• 2003

With the development of multimedia and optical technologies, application systems with facial features hare been increased the interests of researchers, recently. The previous research efforts in face processing mainly use the frontal images in order to recognize human face visually and to extract the facial expression. However, applications, such as image database systems which support queries based on the facial direction and image arrangement systems which place facial images automatically on digital albums, deal with the directional characteristics of a face. In this paper, we propose a method to detect facial directions by using facial features. In the proposed method, the facial trapezoid is defined by detecting points for eyes and a lower lip. Then, the facial direction formula, which calculates the right and left facial direction, is defined by the statistical data about the ratio of the right and left area in facial trapezoids. The proposed method can give an accurate estimate of horizontal rotation of a face within an error tolerance of $\pm1.31$ degree and takes an average execution time of 3.16 sec.

• 한국HCI학회논문지
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• 제6권1호
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• pp.21-26
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• 2011

본 논문은 얼굴 영상을 합성할 때 조명 변화에 강인하도록 조명 보정 기법과 푸아송 영상 처리 기법을 결합한 얼굴 합성 방법을 제시한다. 제시된 방법은 얼굴 영상으로부터 자동적으로 피부 영역을 검출하고, 합성할 부위에서 합성 결과에 영향을 주는 세츄레이션된 부분을 보정한 후 최종적으로 대상 얼굴 영상에 합성하게 된다. 개발된 방법은 카메라가 부착된 모바일 기기에서 촬영된 영상 등에서 자주 발생할 수 있는 조명변화를 보완하여 다양한 얼굴합성 응용 분야에 활용될 수 있다.

• 재활복지공학회논문지
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• 제9권2호
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• pp.129-135
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• 2015

본 연구에서는 3차원 영상처리와 노팅험 스케일을 이용하여 안면마비 평가 시스템을 개발하였다. 시스템은 측정부, 영상처리부, 연산부, 그리고 안면마비 평가 및 출력부로 구성되어 있다. 두 개의 웹캠을 사용하여 안면부의 8곳에 부착된 마커의 3차원 위치를 계산하였으며, 이를 이용하여 노팅험 스케일을 계산하고 화면에 보여준다. 피험자의 자세변화와 측정방식이 노팅험 스케일에 미치는 영향을 조사하였다. 측정방식은 2차원과 3차원을 비교하였으며, 피험자자세는 정면응시와 $11^{\circ}$ 측면응시를 비교하였다. 측면응시한 피험자를 2차원 방식으로 측정한 경우의 오차가 가장 컸다. 3차원 측정방식이 피험자의 자세변화에 따른 오차에 가장 덜 민감하였다.

• Journal of Information Processing Systems
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• 제19권3호
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• pp.323-333
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• 2023

Facial expression recognition can aid in the development of fatigue driving detection, teaching quality evaluation, and other fields. In this study, a facial expression recognition method was proposed with a residual masking reconstruction network as its backbone to achieve more efficient expression recognition and classification. The residual layer was used to acquire and capture the information features of the input image, and the masking layer was used for the weight coefficients corresponding to different information features to achieve accurate and effective image analysis for images of different sizes. To further improve the performance of expression analysis, the loss function of the model is optimized from two aspects, feature dimension and data dimension, to enhance the accurate mapping relationship between facial features and emotional labels. The simulation results show that the ROC of the proposed method was maintained above 0.9995, which can accurately distinguish different expressions. The precision was 75.98%, indicating excellent performance of the facial expression recognition model.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제5권5호
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• pp.251-260
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• 2016

얼굴 인식은 얼굴 영상에서 특징을 추출하고, 이를 다양한 알고리즘을 통해 학습하여 학습된 데이터와 새로운 얼굴 영상에서의 특징과 비교하여 사람을 인식하는 기술로 인식률을 향상시키기 위해서 다양한 방법들이 요구되는 기술이다. 얼굴 인식을 위해 학습 단계에서는 얼굴 영상들로 부터 특징 성분을 추출해야하며, 이를 위한 기존 얼굴 특징 성분 추출 방법에는 선형판별분석(Linear Discriminant Analysis, LDA)이 있다. 이 방법은 얼굴 영상들을 고차원의 공간에서 점들로 표현하고, 클래스 정보와 점의 분포를 분석하여 사람을 판별하기 위한 특징들을 추출하는데, 점의 위치가 얼굴 영상의 화소값에 의해 결정되므로 얼굴 영상에서 불필요한 영역 또는 변화가 자주 발생하는 영역이 포함되는 경우 잘못된 얼굴 특징이 추출될 수 있으며, 특히 일반 카메라 영상을 사용하여 얼굴인식을 수행하는 경우 얼굴과 카메라간의 거리에 따라 얼굴 크기가 다르게 나타나 최종적으로 얼굴 인식률이 저하된다. 따라서 본 논문에서는 이러한 문제점을 해결하기 위해 일반 카메라를 이용하여 얼굴 영역을 검출하고, 검출된 얼굴 영역에서 Gabor Filter를 이용하여 계산된 얼굴 외곽선을 통해 불필요한 영역을 제거한 후 일정 크기로 얼굴 영역 크기를 정규화하였다. 정규화된 얼굴 영상을 선형 판별 분석을 통해 얼굴 특징 성분을 추출하고, 인공 신경망을 통해 학습하여 얼굴 인식을 수행한 결과 기존의 불필요 영역이 포함된 얼굴 인식 방법보다 약 13% 정도의 인식률 향상이 가능하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.111.1-111
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• 2002

System configuration 1. First is the image acquisition part 2. Second part is for creating the vector image and for processing the obtained facial image. This part is for finding the facial area from the skin color. To do this, we can first find the skin color area with the highest weight from eigenface that consists of eigenvector. And then, we can create the vector image of eigenface from the obtained facial area. 3. Third is recognition module portion.