• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.485-492
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• 2018

In this paper, we propose facial expression recognition using CNN (Convolutional Neural Network), one of the deep learning technologies. The proposed structure has general classification performance for any environment or subject. For this purpose, we collect a variety of databases and organize the database into six expression classes such as 'expressionless', 'happy', 'sad', 'angry', 'surprised' and 'disgusted'. Pre-processing and data augmentation techniques are applied to improve training efficiency and classification performance. In the existing CNN structure, the optimal structure that best expresses the features of six facial expressions is found by adjusting the number of feature maps of the convolutional layer and the number of nodes of fully-connected layer. The experimental results show good classification performance compared to the state-of-the-arts in experiments of the cross validation and the cross database. Also, compared to other conventional models, it is confirmed that the proposed structure is superior in classification performance with less execution time.

• Journal of Convergence Korean Medicine
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• v.1 no.1
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• pp.5-15
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• 2021

Objectives: To review the concept of Moebius syndrome. Methods: Literature search was done to study definition, epidemiology, pathophysiology, clinical feature, and treatment of Moebius syndrome. Pubmed, RISS, Google scholarship and uptodate scholastic were used in the research. Search words were 'Moebius syndrome', 'treatment of Moebius syndrome'. Only English and Korean studies were assessed. Results: Moebius syndrome is rare disease characterized by nonprogressive congenital uni- or bi-lateral facial (VII cranial nerve) and abducens (VI cranial nerve) palsy. This facial palsy is found across the world, and its incidence is approximately 1 per 250,000. Moebius is diagnosed by clinical features. Facial palsy, eye abduction problem, limb deformities, global cerebral nerve impairment can be shown. Rehabilitation, smile surgery, and acupuncture can be used to treat this. Conclusion: Moebius syndrome's epidemiology, pathogenesis, treatment is still not fully revealed. It is known to be a congenital disease which didn't have exact treatment except surgery. But, it needs further study about exact treatment, diagnosis, and pathogenesis.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.11-14
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• 2007

인간과 컴퓨터간의 상호교류 하는데 있어서 감정 인식은 필수라 하겠다. 그래서 본 논문에서는 음성 신호 및 얼굴 영상을 BL(Bayesian Learning)과 PCA(Principal Component Analysis)에 적용하여 5가지 감정 (Normal, Happy, Sad, Anger, Surprise) 으로 패턴 분류하였다. 그리고 각각 신호의 단점을 보완하고 인식률을 높이기 위해 결정 융합 방법과 특징 융합 방법을 이용하여 감정융합을 실행하였다. 결정 융합 방법은 각각 인식 시스템을 통해 얻어진 인식 결과 값을 퍼지 소속 함수에 적용하여 감정 융합하였으며, 특정 융합 방법은 SFS(Sequential Forward Selection)특정 선택 방법을 통해 우수한 특정들을 선택한 후 MLP(Multi Layer Perceptron) 기반 신경망(Neural Networks)에 적용하여 감정 융합을 실행하였다.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.363-366
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• 2005

In this paper, for detecting human faces under variations of lighting condition and facial expression, distance ranking feature and detection algorithm based on the feature are proposed. Distance ranking is the intensity ranking of a distance transformed image. Based on statistically consistent edge information, distance ranking is robust to lighting condition change. The proposed detection algorithm is a matching algorithm based on FFT and a solution of discretization problem in the sliding window methods. In experiments, face detection results in the situation of varying lighting condition, complex background, facial expression change and partial occlusion of face are shown

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.761-766
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• 2007

이 논문에서는 커널 Edge Map 방식의 얼굴의 특징점을 검출하는 방법과 Adaboost를 이용한 얼굴의 특징점을 검출하는 방법을 이용하여 좀 더 강인한 얼굴의 특징점을 검출해 낸다. 커널 Edge Map을 이용한 방법은 기존의 10개의 커널을 이용하여 검출된 Edge를 이용하지 않고 좀 더 빠르게 검출해내기 위해 2개의 커널을 이용하여 얼굴의 특징점을 검출해 낸다. 이렇게 만들어진 얼굴의 특징점 후보군들에서 Adaboost를 이용하여 좀 더 정확하고 빠른 특징점을 찾을 수 있게 된다. Adaboost를 이용한 방법은 각각의 특징점들을 오프라인 상에서 학습을 하고 실시간으로 특징점을 검출하는 방법을 사용하였다. Edge를 이용한 방법으로 이미지의 전처리를 하여 후보군을 찾고 그 후보군과 Adaboost를 이용한 후보군들의 조합으로 인해 좀 더 강인하게 얼굴의 특징점을 찾을 수 있다.

• Proceedings of the IEEK Conference
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• 2000.11c
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• pp.113-116
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• 2000

Human face recognition and 3D human face reconstruction has been studied in this paper. To find the facial feature points, find edge from input image and analysis the accumulated histogram of edge information. This paper use a Generic Face Model to display the 3D human face model which was implement with OpenGL and generated with 500 polygons. For reality of 3D human face model, we propose Group matching mapping method between facial feature points and the one of Generic Face Model. The personalized 3D human face model which resembles real human face can be generated automatically in less than 5 seconds on Pentium PC.

• Archives of Plastic Surgery
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• v.40 no.6
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• pp.783-786
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• 2013

For years, the gold standard in facial rejuvenation has been the face lift. However, exploring new, less complex procedures for achieving the same goal is currently drawing interest. Rejuvenation of the perioral area is a difficult task for plastic surgeons because of the minimal effect that face lift procedures have over this region and the lack of published material on the subject. In this article, the descended mouth corner anguloplasty technique is presented. It is a 20-minutes lift technique that can correct this typical feature of the ageing mouth. The authors have treated 71 patients using the technique with consistently good results, with just one requiring revision. They conclude that this procedure by itself and in combination with other small operations or even a full face lift can rejuvenate the ageing face.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2016-2019
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• 2003

In this paper, we take some features for face recognition out of face image, using a simple type of templates. We use the extracted features to do Adaboost learning for face recognition. Using a carefully-chosen feature among these features, we can make a weak face classifier for face recognition. And doing Adaboost learning on and on with those chosen several weak classifiers, we can get a strong face classifier. By using Adaboost Loaming, we can choose particular features which is not easily subject to changes in illumination and facial expression about several images of one person, and construct face recognition system. Therefore, the face classifier bulit like the above way has robustness in both facial expression and illumination variation, and it finally gives capability of recognizing face fast due to the simple feature.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.8
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• pp.60-67
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• 1998

We studied the effects of fractional fourier transform in face recognition, in which only the amplitude spectra of transformed facial images were used.We used two recently developed face recognition methods, the most effective feature (MEF) method (i.e., eigenface method) and most discriminating feature (MDF) method, and the effects of th etransform for th etwo methods were consistent. We confirmed that the recognition rate by the use of MDF method is better than that consistent. We confirmed that the recognition rate by the use of MDF method is better than that by MEF regardless of the order to transform, these methods provided slightly better results when the order was 1 than for any other order values. Only when the order was close to 1, the recognition rates were robust to the shift of the input images, and the trend that the recognition rates decreased as the input size varied was independent of the order. From these results, we fond that it is most advantageous to use the amplitude spectra of the conventional fourier transform whose order is 1.