• 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.

• 한국멀티미디어학회논문지
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• 제15권1호
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• pp.40-50
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• 2012

Face recognition is a biometric technology used to identify individuals based on facial feature information. Previous studies of face recognition used features including the eye, mouth and nose; however, there have been few studies on the effects of using other facial components, such as the eyebrows and chin, on recognition performance. We measured the recognition accuracy affected by these facial components, and compared the differences between computer-based and human-based facial recognition methods. This research is novel in the following four ways compared to previous works. First, we measured the effect of components such as the eyebrows and chin. And the accuracy of computer-based face recognition was compared to human-based face recognition according to facial components. Second, for computer-based recognition, facial components were automatically detected using the Adaboost algorithm and active appearance model (AAM), and user authentication was achieved with the face recognition algorithm based on principal component analysis (PCA). Third, we experimentally proved that the number of facial features (when including eyebrows, eye, nose, mouth, and chin) had a greater impact on the accuracy of human-based face recognition, but consistent inclusion of some feature such as chin area had more influence on the accuracy of computer-based face recognition because a computer uses the pixel values of facial images in classifying faces. Fourth, we experimentally proved that the eyebrow feature enhanced the accuracy of computer-based face recognition. However, the problem of occlusion by hair should be solved in order to use the eyebrow feature for face recognition.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.280-286
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• 2022

Facial recognition system is a biometric manipulation. Its applicability is simpler, and its work range is broader than fingerprints, iris scans, signatures, etc. The system utilizes two technologies, such as face detection and recognition. This study aims to develop a facial recognition system to recognize person's faces. Facial recognition system can map facial characteristics from photos or videos and compare the information with a given facial database to find a match, which helps identify a face. The proposed system can assist in face recognition. The developed system records several images, processes recorded images, checks for any match in the database, and returns the result. The developed technology can recognize multiple faces in live recordings.

• 제어로봇시스템학회논문지
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• 제12권3호
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• pp.255-263
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• 2006

In the last decade, face analysis, e.g. face detection, face recognition, facial expression recognition, is a very lively and expanding research field. As computer animated agents and robots bring a social dimension to human computer interaction, interest in this research field is increasing rapidly. In this paper, we introduce an artificial emotion mimic system which can recognize human facial expressions and also generate the recognized facial expression. In order to recognize human facial expression in real-time, we propose a facial expression classification method that is performed by weak classifiers obtained by using new rectangular feature types. In addition, we make the artificial facial expression using the developed robotic system based on biological observation. Finally, experimental results of facial expression recognition and generation are shown for the validity of our robotic system.

• ETRI Journal
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• 제35권6호
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• pp.1021-1028
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• 2013

Automatic facial expression recognition is a widely studied problem in computer vision and human-robot interaction. There has been a range of studies for representing facial descriptors for facial expression recognition. Some prominent descriptors were presented in the first facial expression recognition and analysis challenge (FERA2011). In that competition, the Local Gabor Binary Pattern Histogram Sequence descriptor showed the most powerful description capability. In this paper, we introduce hybrid facial representations for facial expression recognition, which have more powerful description capability with lower dimensionality. Our descriptors consist of a block-based descriptor and a pixel-based descriptor. The block-based descriptor represents the micro-orientation and micro-geometric structure information. The pixel-based descriptor represents texture information. We validate our descriptors on two public databases, and the results show that our descriptors perform well with a relatively low dimensionality.

• 한국지능시스템학회논문지
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• 제16권6호
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• pp.772-776
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• 2006

얼굴 표정인식에 관한 연구는 대부분 얼굴의 정면 화상을 가지고 연구를 한다. 얼굴 표정인식에 큰 영향을 미치는 대표적인 부위는 눈과 입이다. 그래서 표정 인식 연구자들은 눈, 눈썹, 입을 중심으로 표정 인식이나 표현 연구를 해왔다. 그러나 일상생활에서 카메라 앞에서는 대부분의 사람들은 눈동자의 빠른 변화의 인지가 어렵다. 또한 많은 사람들이 안경을 쓰고 있다. 그래서 본 연구에서는 눈이 가려진 경우의 표정 인식을 Principal Component Analysis (PCA)를 이용하여 시도하였다.

• 반도체디스플레이기술학회지
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• 제18권1호
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• pp.97-101
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• 2019

Understanding and classification of the human's emotion play an important tasks in interacting with human and machine communication systems. This paper proposes a novel emotion recognition method by extracting facial keypoints, which is able to understand and classify the human emotion, using active Appearance Model and the proposed classification model of the facial features. The existing appearance model scheme takes an expression of variations, which is calculated by the proposed classification model according to the change of human facial expression. The proposed method classifies four basic emotions (normal, happy, sad and angry). To evaluate the performance of the proposed method, we assess the ratio of success with common datasets, and we achieve the best 93% accuracy, average 82.2% in facial emotion recognition. The results show that the proposed method effectively performed well over the emotion recognition, compared to the existing schemes.

• 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.

• 정보처리학회논문지B
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• 제16B권5호
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• pp.395-402
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• 2009

본 연구는 표정 인식률을 개선하기 위한, 강도가 다른 표정을 인식하는 새로운 표정 인식 방법을 제안한다. 사람마다 다르게 나타나는 표정과 표정마다 다른 강도는 표정 인식률 저하에 지대한 영향을 미친다. 하지만 얼굴 표정의 다양한 강도를 처리하는 방법은 많이 제시되지 않고 있다. 본 연구에서는 표정 템플릿과 표정 강도 분포모델을 이용하여 다양한 얼굴 표정 강도를 인식하는 방법을 제시한다. 표정 템플릿과 표정강도 분포모델은 얼굴의 특징 부위에 표시한 관심 점과 얼굴 특징 부위간의 움직임이 다른 표정과 강도에 따라 어떻게 달라지는지 설명하여 표정 인식률 개선에 기여한다. 제안 방법은 정지 이미지뿐만 아니라 비디오시퀀스에서도 빠른 측정 과정을 통해 다양한 강도의 표정을 인식할 수 있는 장점이 있다. 실험 결과, 제안 연구가 특히 약한 강도의 표정에 대해 타 방법보다 높은 인식 결과를 보여 제안 방법이 다양한 강도의 표정 인식에 강건함을 알 수 있다.

• 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.