• 한국산업정보학회논문지
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• 제22권6호
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• pp.39-45
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• 2017

기존의 색상기반 얼굴인식 방법은 조명변화에 민감하며, 위변조의 가능성이 있기 때문에 다양한 산업분야에 적용되기 어려운 문제가 있었다. 본 논문에서는 이러한 문제를 해결하기 위해 깊이 영상을 이용한 지역 이진 패턴(LBP) 기반의 얼굴인식 방법을 제안한다. 깊이 정보를 이용한 얼굴 검출 방법과 얼굴 인식을 위한 특징 추출 및 매칭 방법을 구현하고, 모의실험 결과를 바탕으로 제안된 방식의 인식 성능을 나타낸다.

• 한국멀티미디어학회논문지
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• 제14권8호
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• pp.992-999
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• 2011

This paper proposed a human body posture recognition program based on haar-like feature and hand activity detection. Its distinguishing features are the combination of face detection and motion detection. Firstly, the program uses the haar-like feature face detection to receive the location of human face. The haar-like feature is provided with the advantages of speed. It means the less amount of calculation the haar-like feature can exclude a large number of interference, and it can discriminate human face more accurately, and achieve the face position. Then the program uses the frame subtraction to achieve the position of human body motion. This method is provided with good performance of the motion detection. Afterwards, the program recognises the human body motion by calculating the relationship of the face position with the position of human body motion contour. By the test, we know that the recognition rate of this algorithm is more than 92%. The results show that, this algorithm can achieve the result quickly, and guarantee the exactitude of the result.

• 스마트미디어저널
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• 제7권4호
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• pp.61-69
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• 2018

Multi-tracking of general objects and specific faces is an important topic in the field of computer vision applicable to many branches of industry such as biometrics, security, etc. The rapid development of deep neural networks has resulted in a dramatic improvement in face recognition and object detection problems, which helps improve the multiple-face tracking techniques exploiting the tracking-by-detection method. Our proposed method uses face detection trained with a head dataset to resolve the face deformation problem in the tracking process. Further, we use robust face features extracted from the deep face recognition network to match the tracklets with tracking faces using Hungarian matching method. We achieved promising results regarding the usage of deep face features and head detection in a face tracking benchmark.

• Journal of Multimedia Information System
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• 제2권1호
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• pp.171-178
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• 2015

This article describes 3 dimensional (3D) face recognition system using histogram of oriented gradients (HOG) based on face curvature. The surface curvatures in the face contain the most important personal feature information. In this paper, 3D face images are recognized by the face components: cheek, eyes, mouth, and nose. For the proposed approach, the first step uses the face curvatures which present the facial features for 3D face images, after normalization using the singular value decomposition (SVD). Fisherface method is then applied to each component curvature face. The reason for adapting the Fisherface method maintains the surface attribute for the face curvature, even though it can generate reduced image dimension. And histogram of oriented gradients (HOG) descriptor is one of the state-of-art methods which have been shown to significantly outperform the existing feature set for several objects detection and recognition. In the last step, the linear discriminant analysis is explained for each component. The experimental results showed that the proposed approach leads to higher detection accuracy rate than other methods.

• 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.

• 한국정보전자통신기술학회논문지
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• 제13권3호
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• pp.206-211
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• 2020

얼굴검출 과정은 영상 모니터링에서 매우 중요한 과정이며 생체 인식 기술의 한 종류이다. 검출과정은 변수가 많고 복잡하여 하드웨어가 발전하고 있는 근래에 와서 소프트웨어적인 발전이 이루어지고 있다. CCTV를 이용하는 분야 중 얼굴 검출 기술은 얼굴을 분석하기 이전에 실행되는 과정으로 영상에서 얼굴이 있는 곳을 찾아내는 기술이다. 사람의 얼굴은 조명이나 피부 색, 방향과 각도, 표정 등 여러 가지 환경적 조건에 따라 민감한 반응을 하기 때문에, 얼굴 검출에 관한 연구는 많은 어려움이 있다. 얼굴 검출 기술의 활용성과 중요성은 시간이 지날수록 각광받고 있으나, 얼굴 검출 이전에 선행되어야 하는 얼굴 영역 검출 기술에 대해서는 간과하는 측면이 많다. 본 논문의 시스템은 AdaBoost detector에서 검출 못하는 기울어진 얼굴을 검출할 수 있어 다른 사물의 검출도 같은 기술을 사용할 수 있을 것이다.

• 한국멀티미디어학회논문지
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• 제11권12호
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• pp.1676-1687
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• 2008

In this paper, we proposed a new multi-view human recognition method based on face and gait features detection algorithm. For getting the position of moving object, we used the different of two consecutive frames. And then, base on the extracted object, the first important characteristic, walking direction, will be determined by using the contour of head and shoulder region. If this individual appears in camera with frontal direction, we will use the face features for recognition. The face detection technique is based on the combination of skin color and Haar-like feature whereas eigen-images and PCA are used in the recognition stage. In the other case, if the walking direction is frontal view, gait features will be used. To evaluate the effect of this proposed and compare with another method, we also present some simulation results which are performed in indoor and outdoor environment. Experimental result shows that the proposed algorithm has better recognition efficiency than the conventional sing]e view recognition method.

• 대한임베디드공학회논문지
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• 제9권5호
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• pp.261-268
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• 2014

Face detection is essential to the full automation of face image processing application system such as face recognition, facial expression recognition, age estimation and gender identification. It is found that local image features which includes Haar-like, LBP, and MCT and the Adaboost algorithm for classifier combination are very effective for real time face detection. In this paper, we present a face detection method using local pixel direction code(PDC) feature and lookup table classifiers. The proposed PDC feature is much more effective to dectect the faces than the existing local binary structural features such as MCT and LBP. We found that our method's classification rate as well as detection rate under equal false positive rate are higher than conventional one.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2008년도 학술대회 3부
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• pp.172-177
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• 2008

생체 인식은 개인의 고유한 생체 정보를 획득하여 개인 식별에 이용하는 기술로, 그중 얼굴 인식은 사용자의 편의성과 비강제성이라는 장점이 있는 응용기술로 평가 받고 있다. 본 논문에서는 얼굴인식 기술동향을 살펴보고 얼굴 영역 추출, 특정 추출, 매칭을 포함한 시스템에 대해 논한다. 얼굴 영역 추출에는 얼굴 형판 정합 방법과 얼굴 요소의 검출에 의한 방법을, 특정 추출에서는 PCA 와 LDA 등의 방법을, 그리고 매칭을 통한 인증 단계에서는 최근접 분류기를 소개한다. 다양한 얼굴 인식 기법들이 제시됨에 따라 공인된 성능 평가 방법이 필요하게 되는데, 대용량 표준 얼굴 DE의 구축과 얼굴 인식 성능 평가 방법 개발의 필요성을 제시한다. 향후 얼굴인식 시스템에서는 조명, 자세, 표정의 변화를 어떻게 보정하여 인식 할 것인가 하는 것이 연구되어야 할 핵심 분야로서 3차원 얼굴 영상 복원 기술을 통한 해결방법을 살펴본다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.575-578
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• 1999

The principal features of a face are as follows : skin-tone, symmetry, and requisites such as shape of ellipse, eyes, nose, mouth. Also, faces have different size, various shape and position. In case of application of face recognition and detection without preprocessing, efficiency of the performance is decreased. In addition, face itself, complex background, image quality, etc. are included. Therefore, previous face recognition methods are implemented on the base of specific constraints of the face image. In this paper, we propose the efficient and automatic face detection algorithm for minimizing influence such as complex background, image quality, etc. This face detection technique consists of skin-tone, candidate face region and face region extractions.