• Journal of International Society for Simulation Surgery
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• 제2권1호
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• pp.7-9
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• 2015

Purpose In the many face-related application such as head pose estimation, 3D face modeling, facial appearance manipulation, the robust and fast facial feature extraction is necessary. We present the facial feature extraction method based on shape regression and feature selection for real-time facial feature extraction. Materials and Methods The facial features are initialized by statistical shape model and then the shape of facial features are deformed iteratively according to the texture pattern which is selected on the feature pool. Results We obtain fast and robust facial feature extraction result with error less than 4% and processing time less than 12 ms. The alignment error is measured by average of ratio of pixel difference to inter-ocular distance. Conclusion The accuracy and processing time of the method is enough to apply facial feature based application and can be used on the face beautification or 3D face modeling.

• 산업융합연구
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• 제13권2호
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• pp.45-54
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• 2015

This Study aims to analyze the factors for the success of commercialization of the facial extraction and recognition image security information system of the domestic companies in Korea. As the results of the analysis, the internal factors for the success of commercialization of the facial extraction and recognition image security information system of the company were found to include (1) Holding of technology for close range facial recognition, (2) Holding of several facial recognition related patents, (3) Preference for the facial recognition security system over the fingerprint recognition and (4) strong volition of the CEO of the corresponding company. On the other hand, the external environmental factors for the success were found to include (1) Extensiveness of the market, (2) Rapid growth of the global facial recognition market, (3) Increased demand for the image security system, (4) Competition in securing of the engine for facial extraction and recognition and (5) Selection by the government as one of the 100 major strategic products.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.147-149
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• 2004

There are no authentic solutions in a face region extraction problem though it is an important part of pattern recognition and has diverse application fields. It is not easy to develop the facial region extraction algorithm because the facial image is very sensitive according to age, sex, and illumination. In this paper, to solve these difficulties, a fuzzy color filer based on the facial region extraction algorithm is proposed. The fuzzy color filter makes the robust facial region extraction enable by modeling the skin color. Especially, it is robust in facial region extraction with various illuminations. In addition, to identify the fuzzy color filter, a linear matrix inequality(LMI) optimization method is used. Finally, the simulation result is given to confirm the superiority of the proposed algorithm.

• 제어로봇시스템학회논문지
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• 제16권10호
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• pp.957-962
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• 2010

If we want to recognize the human's emotion via the facial image, first of all, we need to extract the emotional features from the facial image by using a feature extraction algorithm. And we need to classify the emotional status by using pattern classification method. The AAM (Active Appearance Model) is a well-known method that can represent a non-rigid object, such as face, facial expression. The Bayesian Network is a probability based classifier that can represent the probabilistic relationships between a set of facial features. In this paper, our approach to facial feature extraction lies in the proposed feature extraction method based on combining AAM with FACS (Facial Action Coding System) for automatically modeling and extracting the facial emotional features. To recognize the facial emotion, we use the DBNs (Dynamic Bayesian Networks) for modeling and understanding the temporal phases of facial expressions in image sequences. The result of emotion recognition can be used to rehabilitate based on biofeedback for emotional disabled.

• 한국컴퓨터산업학회논문지
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• 제4권10호
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• pp.631-642
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• 2003

본 논문에서는 웹상에서 자신을 대신하는 아바타 제작시 본인의 얼굴과 닮은 얼굴을 생성하기 위해 사진으로부터 개인의 특징정보를 추출하는 방법과 추출된 특징정보에 따라 해당하는 이목구비를 준비된 분류기준에 의해 특정 클래스로 분류해 내는 방법을 제안한다. 특징정보 추출은 눈, 코, 입, 턱선으로 나누어 진행되어졌으며, 각 이목구비의 특징점과 분류기준을 각각 제시하였다. 추출 된 특징정보들은 전문 디자이너에 의해 그려진 이목구비 이미지들과 유사도를 계산하는데 사용되었으며, 여기서 가장 유사한 이미지를 턱선 벡터이미지에 합성하여 아바타 얼굴을 얻어낼 수 있었다.

• Journal of Acupuncture Research
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• 제33권2호
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• pp.211-220
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• 2016

Objectives : Facial nerve palsy is a rare but well-known complication that occurs after a tooth extraction. The paralysis follows the injection of a local anesthetic, but patients typically recover after a few hours. However, there are a number of reports of delayed paralysis, and the cause of delayed facial palsy remains uncertain. This study is the first case report detailing how Korean medicine can be used to treat facial nerve palsy following tooth extraction. This study reports our experience of a patient's favorable recovery. Methods : A 25-year-old male patient experienced acute facial palsy after four premolar teeth were extracted. He was hospitalized in the Pusan National University Korean Medical Hospital. We provided complex Korean traditional medical treatments such as acupuncture, cupping, use of a hot water steamer, and herbal medicine for 18 days. Results : Using the Yanagihara Grading Score, we found improvements in the patient's voluntary facial movement as his score increased from 22 to 34. Furthermore, his accompanying symptoms, such as dry eye and facial pain, disappeared. However, the patient reported transient pain around acupoints after the acupuncture intervention. Conclusion : Our study suggests that Korean medical treatments might be effectively used to treat facial nerve palsy after tooth extraction, although further research should be conducted due to the limited number of cases in this area.

• 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.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.425-428
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• 2009

We have researched and developed the caricature generation system PICASSO. PICASSO outputs the deformed facial caricature by comparing input face with prepared mean face. We specialized it as PICASSO-2 for exhibiting a robot at Aichi EXPO2005. This robot enforced by PICASSO-2 drew a facial caricature on the shrimp rice cracker with the laser pen. We have been recently exhibiting another revised robot characterized by a brush drawing. This system takes a couple of facial images with CCD camera, extracts the facial features from the images, and generates the facial caricature in real time. We experimentally evaluated the performance of the caricatures using a lot of data taken in Aichi EXPO2005. As a result it was obvious that this system were not sufficient in accuracy of eyebrow region extraction and mouth detection. In this paper, we propose the improved methods for eyebrow region extraction and mouth detection.

• 인터넷정보학회논문지
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• 제5권5호
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• pp.79-91
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• 2004

본 논문에서는 음영합성 기법과 얼굴 인식 기술 중 특징추출 기법을 이용한 아바타 얼굴 자동생성 시스템을 제안한다. 제안하는 시스템은 사진으로부터 얼굴의 특징정보를 추출하여 사람의 얼굴과 유사한 아바타 얼굴을 자동으로 생성해 주는 시스템이며, 음영을 사진으로부터 추출하여 이를 각 이목구비 이미지와 합성하여 생성한다. 따라서 실사형에 좀 더 근접한 얼굴을 생성할 수 있다. 본 논문은 새로운 눈동자 추출 기법과 각 이목구비별 특징정보 추출 방법 그리고. 검색시간을 줄이기 위한 분류 방법, 유사도 계산에 의한 이미지 검색방법, 최종적으로 사진으로부터 음영을 추출하여 검색된 이목구비와 합성, 실사형 아바타 얼굴을 생성하는 방법을 제안한다.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2007년도 학술대회 1부
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• pp.667-674
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• 2007

Facial expressions provide significant clues about one's emotional state; however, it always has been a great challenge for machine to recognize facial expressions effectively and reliably. In this paper, we report a method of feature-based adaptive motion energy analysis for recognizing facial expression. Our method optimizes the information gain heuristics of ID3 tree and introduces new approaches on (1) facial feature representation, (2) facial feature extraction, and (3) facial feature classification. We use minimal reasonable facial features, suggested by the information gain heuristics of ID3 tree, to represent the geometric face model. For the feature extraction, our method proceeds as follows. Features are first detected and then carefully "selected." Feature "selection" is finding the features with high variability for differentiating features with high variability from the ones with low variability, to effectively estimate the feature's motion pattern. For each facial feature, motion analysis is performed adaptively. That is, each facial feature's motion pattern (from the neutral face to the expressed face) is estimated based on its variability. After the feature extraction is done, the facial expression is classified using the ID3 tree (which is built from the 1728 possible facial expressions) and the test images from the JAFFE database. The proposed method excels and overcomes the problems aroused by previous methods. First of all, it is simple but effective. Our method effectively and reliably estimates the expressive facial features by differentiating features with high variability from the ones with low variability. Second, it is fast by avoiding complicated or time-consuming computations. Rather, it exploits few selected expressive features' motion energy values (acquired from intensity-based threshold). Lastly, our method gives reliable recognition rates with overall recognition rate of 77%. The effectiveness of the proposed method will be demonstrated from the experimental results.