• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.127-133
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• 2021

In this study, an augmented reality-based make-up tool was built to analyze the user's face shape based on face-type reference model data and to provide virtual makeup by providing face-type makeup. To analyze the face shape, first recognize the face from the image captured by the camera, then extract the features of the face contour area and use them as analysis properties. Next, the feature points of the extracted face contour area are normalized to compare with the contour area characteristics of each face reference model data. Face shape is predicted and analyzed using the distance difference between the feature points of the normalized contour area and the feature points of the each face-type reference model data. In augmented reality-based virtual makeup, in the image input from the camera, the face is recognized in real time to extract the features of each area of the face. Through the face-type analysis process, you can check the results of virtual makeup by providing makeup that matches the analyzed face shape. Through the proposed system, We expect cosmetics consumers to check the makeup design that suits them and have a convenient and impact on their decision to purchase cosmetics. It will also help you create an attractive self-image by applying facial makeup to your virtual self.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.401-404
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• 2012

기존 얼굴 인식 알고리즘은 단일 특징 기반의 전역적 방식이었다. 정확도를 향상시키기 위해 복수의 특징점을 이용하는 방법들이 제안되었으나 이는 알고리즘의 복잡도가 증가하고, 계산 속도가 느린 단점이 있다. 본 논문에서는 대표적인 특징점 추출 알고리즘인 SURF (Speeded Up Robust Features)를 이용한 얼굴 인식 방법을 제안한다. SURF 를 통해 기술어를 추출하고, Gabor 특징과 LBP 특징을 이용해 해당 특징점에서 기술어를 추출함으로써 기존 알고리즘보다 경량화할 수 있고, 수행시간을 줄일 수 있다. 잘 알려진 ORL 데이터베이스에서의 실험에서 제안한 방법이 정합시간을 포함한 수행 시간에서 약 16%의 감소를 보였고, 정확도 또한 약 34% 향상되었다.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.146-149
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• 2003

개인의 신분을 확인하는 생체인식기술은 정보기술이 고도화된 사회 속에서 정보보안의 관점에서 더욱 중요한 문제로 인식되기 시작했다. 이러한 생체인식 영역 중에서 비교적 거부감을 덜 주면서 어느 정도의 인식율을 제공하는 얼굴인식 분야의 연구는 지난 수년간 활발하게 진행되었다. 보통 전통적으로 얼굴인식에는 우선 PCA가 적용되어 데이터를 축소하고 LDA가 얼굴 인식을 위한 특징벡터를 추출하는 역할을 수행한다 본 논문에서는 이러한 이원적인 과정을 동시 대각화를 통해 하나의 과정으로 통합하고 기존의 유클리디언 디스턴스 대신에 SVM(Support Vector Machine) 패턴 분류기를 사용하여 얼굴인식을 수행하는 알고리즘을 제안한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.528-531
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• 2004

본 논문에서는 칼라 영상을 이용하여 인간의 감성을 인식할 수 있는 방법을 제안한다. 먼저 칼라 영상으로부터 피부색 추출방법을 이용하여 얼굴을 추출한다 그 다음, 추출된 얼굴 영상으로부터 인간 얼굴의 특징 점(눈썹, 눈, 코, 입)들을 추출하는 방법과 각 특징 점들 간의 구조적인 관계로부터 인간의 감성(기쁨, 놀람, 슬픔, 분노)을 인식하는 방법을 제안한다. 본 논문에서 제안한 방법은 퍼지 추론을 기반으로 하여 인간의 감성을 인식한다. 마지막으로, 제안된 방법은 실험을 통해 그 응용 가능성을 확인한다.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.13-20
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• 2011

The face recognition has been used in a variety fields, such as identification and security. The procedure of the face recognition is as follows; extracting face features of face images, learning the extracted face features, and selecting some features among all extracted face features. The selected features have discrimination and are used for face recognition. However, there are numerous face features extracted from face images. If a face recognition system uses all extracted features, a high computing time is required for learning face features and the efficiency of computing resources decreases. To solve this problem, many researchers have proposed various Boosting methods, which improve the performance of learning algorithms. Mutual-Boost is the typical Boosting method and efficiently selects face features by using mutual information between two features. In this paper, we propose a GroupMutual-Boost method for improving Mutual-Boost. Our proposed method can shorten the time required for learning and recognizing face features and use computing resources more effectively since the method does not learn individual features but a feature group.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.2
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• pp.47-54
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• 2001

The main purpose of this study is to propose the algorithm about the extraction of the facial feature. To achieve the above goal, first of all, this study produces binary image for input color image. It calculates area after pixel labeling by variant block-units. Secondly, by contour following, circumference have been calculated. So the proper degree of resemblance about area, circumference, the proper degree of a circle and shape have been calculated using the value of area and circumference. And Third, the algorithm about the methods of extracting parameters which are about the feature of eyes, nose, and mouse using the proper degree of resemblance, general structures and characteristics(symmetrical distance) in face have been accomplished. And then the feature parameters of the front face have been extracted. In this study, twelve facial feature parameters have been extracted by 297 test images taken from 100 people, and 92.93 % of the extracting rate has been shown.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05d
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• pp.609-613
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• 2002

본 논문에서는 효과적인 비디오 인덱싱을 위해 얼굴 영역 추출을 통한 장면 전환 검출 방법을 제시하였다. 히스토그램과 사람의 피부색 검출을 통해 사람의 얼굴을 포함하는 후보 프레임을 찾고, 얼굴 영역과 특징 추출을 통해 사람을 포함하는 키 프레임을 검출하여 비디오의 장면 전환 프레임을 검출하고, 실험을 통해 제안된 방법의 우수성을 보였다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.1
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• pp.55-60
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• 2014

In this paper, we proposed a method for face region extraction by skin-color hue, saturation and facial feature extraction in natural images. The proposed algorithm is composed of lighting correction and face detection process. In the lighting correction step, performing correction function for a lighting change. The face detection process extracts the area of skin color by calculating Euclidian distances to the input images using as characteristic vectors color and chroma in 20 skin color sample images. Eye detection using C element in the CMY color model and mouth detection using Q element in the YIQ color model for extracted candidate areas. Face area detected based on human face knowledge for extracted candidate areas. When an experiment was conducted with 10 natural images of face as input images, the method showed a face detection rate of 100%.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.546-549
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• 2005

본 논문에서는 잡음제거와 주요성분분석을 이용한 효과적인 얼굴인식 기법을 제안하였다. 여기서 잡음제거는 필터링과 1차 모멘트 평형이동을 조합하여 영상의 특징정보와 관계가 없는 배경을 제거함을 위한 것이고, 주요성분분석은 얼굴영상의 주요성분인 2차원의 중복성분이 제거된 특징을 효과적으로 추출하기 위함이다. 제안된 기법을 768*576 픽셀 크기를 갖는 24개의 AR얼굴영상을 대상으로 시뮬레이션한 결과, 제안된 얼굴인식이 잡음제거를 하지 않은 기존의 얼굴인식에 비해 주요성분의 개수에 따른 압축성능, 특징추출 시간, 그리고 city-block, Euclidean, negative angle(cosine)의 거리척도에 따른 인식에 있어서 보다 우수한 성능이 있음을 확인할 수 있었다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.2
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• pp.7-12
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• 2009

The new approach was proposed which uses feature extraction based on fuzzy integral in the process of face verification using morphological shape decomposition. The centre of area was used with image pixels related with structure element and its weight in an attempt to consider neighborhood information. Therefore the morphological operators were defined for feature extraction. And then the number of decomposition images were more about 4 times than the conventional. Finally in the simulations with the extractions for face verification it was proved that the approach in this paper was even more good than the conventional in stability of feature extraction and threshold value.