• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.346-357
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• 2008

관상학(Physiognomy)이란 사람의 얼굴을 보고 그의 운명, 성격, 수명 따위를 판단하는 방법을 연구하는 학문을 말한다. 이 논문에서 언급하는 관상학은 동양에서 말하는 관상학, 특히 얼굴의 부분적 특성이나 전체적인 조화를 통해 성격과 운영을 예측하는 학문을 의미한다. 이 연구는 동양 관상학을 적용한 성격별 얼굴 설계 시스템 구축에 관한 것으로, 첫째, 보편적인 성격 분류를 위해 MBTI에서 다루는 성격 어휘 161개를 군집분석을 통해 39개의 대표 어휘로 추출하였다. 추출된 대표 성격 어휘의 의미상 거리를 나타내기 위하여 서베이를 통해 얻은 데이터를 다차원 척도법을 통해 2차원 공간상에 성격 어휘의 관계를 분석하였다. 둘째, 얼굴 시각화를 위해 먼저 얼굴의 형태적 특성을 결정짓는 요소를 크게 얼굴형, 눈, 코, 입, 이마, 눈썹으로 분류하고, 분류된 6가지 얼굴 형태의 29가지 하위요소 별 성격을 한국인의 얼굴 특성을 기준으로 관상학적 정리 및 숫자형식 코드화를 하였다. 추출된 대표 성격 어휘별 얼굴 요소의 형태를 앞서 정리된 코드에 따라 하나의 얼굴 형태로 조합하여 39가지 얼굴을 시각화 하여 마지막으로, 성격별 얼굴 설계 시스템 'FACE'를 제작하였다. 이 연구는 사람의 성격 특성에 따라 그에 맞는 얼굴 형태를 구현하는 시스템을 제작하여 일반 사용자 뿐 아니라 애니메이션 캐릭터 개발자에게 객관적인 도움을 줄 수 있으며 또한 예로부터 내려오는 관상학의 적용 범위를 넓힐 수 있는 가능성을 보여주었다고 할 수 있다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.155-164
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• 2004

Recently, we have remarkable developments in intelligent robot systems. The remarkable features of intelligent robot are that it can track user and is able to doface recognition, which is vital for many surveillance-based systems. The advantage of face recognition compared with other biometrics recognition is that coerciveness and contact that usually exist when we acquire characteristics do not exist in face recognition. However, the accuracy of face recognition is lower than other biometric recognition due to the decreasing in dimension from image acquisition step and various changes associated with face pose and background. There are many factors that deteriorate performance of face recognition such as thedistance from camera to the face, changes in lighting, pose change, and change of facial expression. In this paper, we implement a new sliding active camera system to prevent various pose variation that influence face recognition performance andacquired frontal face images using PCA and HMM method to improve the face recognition. This proposed face recognition algorithm can be used for intelligent surveillance system and mobile robot system.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.299-302
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• 2006

본 논문에서는 다중필터와 복합형 신경망으로 구성된 얼굴 검출 시스템과 WFMM 신경망을 이용한 피부색 검출기법을 소개한다. 전처리 단계에 해당하는 다중필터는 대상 영역의 수를 감소 시켜 시스템의 속도를 개선한다. 다중필터에 속한 색상필터는 총 11 가지의 색상 공간에서 피부색의 특징 값을 추출하여 학습 데이터로 사용하며, 이 학습 데이터에 의해 생성된 하이퍼 박스를 통해 피부색을 분류한다. 또한 WFMM 신경망의 연관도 요소 특성을 이용하여 각 색상 공간의 상대적 중요도를 분석하여 피부색 검출에 유용한 색상 공간을 분석하고 추출 한다. 얼굴패턴 검출을 위한 복합형 신경망은 첫 단계에서 가보 변환을 사용하는 CNN 을 통해 특징 지도를 생성하고, WFMM 신경망으로 최종 얼굴패턴을 검증한다.

• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1247-1259
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• 2005

This paper proposes a real-time face recognition system which detects multiple faces from low resolution video such as web-camera video. Face recognition system consists of the face detection step and the face classification step. At First, it finds face region candidates by using AdaBoost based object detection method which have fast speed and robust performance. It generates reduced feature vector for each face region candidate by using principle component analysis. At Second, Face classification used Principle Component Analysis and multi-SVM. Experimental result shows that the proposed method achieves real-time face detection and face recognition from low resolution video. Additionally, We implement the auto-tracking face recognition system using the Pan-Tilt Web-camera and radio On/Off digital door-lock system with face recognition system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.4
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• pp.737-742
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• 2013

The surveillance system has been developed to be intelligent which can judge and cope by itself using human recognition technique. The existing face recognition is excellent at a short distance but recognition rate is reduced at a long distance. In this paper, we analyze the performance of face recognition according to interpolation and face recognition algorithm in face recognition using the multiple distance face images to training. we use the nearest neighbor, bilinear, bicubic, Lanczos3 interpolations to interpolate face image and PCA and LDA to face recognition. The experimental results show that LDA-based face recognition with bilinear interpolation provides performance in face recognition.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.9
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• pp.22-30
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• 2011

Recently, the rising demand for intelligent video surveillance system leads to high-performance face recognition systems. The solution for low-resolution images acquired by a long-distance camera is required to overcome the distance limits of the existing face recognition systems. For that reason, this paper proposes a hardware design of an image resolution enhancement algorithm for real-time intelligent video surveillance systems. The algorithm is synthesizing a high-resolution face image from an input low-resolution image, with the help of a large collection of other high-resolution face images, called training set. When we checked the performance of the algorithm at 32bit RISC micro-processor, the entire operation took about 25 sec, which is inappropriate for real-time target applications. Based on the result, we implemented the hardware module and verified it using Xilinx Virtex-4 and ARM9-based embedded processor(S3C2440A). The designed hardware can complete the whole operation within 33 msec, so it can deal with 30 frames per second. We expect that the proposed hardware could be one of the solutions not only for real-time processing at the embedded environment, but also for an easy integration with existing face recognition system.

• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.71-80
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• 2007

In this paper, We propose face type classification, tongue region extraction and tongue color analysis method for Oriental medicine diagnosis system to supply web based medical treatment information. This presents to construct system that takes super aging society and uses ocular inspection and longue diagnosis in web-based to embody this by an IT Technology as generalization and popularization of medical benefit are social requirement and supplies medical treatment information. Place that reflect living body signal of human body ordinarily and appear becomes iris or tongue, five sensory organs etc. This paper proposes classification of face type, extraction of five sensory organs for observing a person's shape and color among diseases diagnosis based on home health care that propose to develop and region extraction and color analysis etc, of tongue which intensively represents the bio-signals of human-beings. Finally, the effectiveness of this paper is verified by several experiments.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.11a
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• pp.15-18
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• 2011

본 논문에서는 이동형 패럴랙스 배리어 방식의 모바일 3D 디스플레이에 응용하기 위해 개발된 시역계측알고리즘[1]을 실제시스템에 구현한 후 문제점을 분석하고, 그 문제점을 해결할 수 있는 새로운 방법을 제안한다. 본 연구팀에서 이동형 패럴랙스 배리어 방식의 모바일 3D 디스플레이에 응용하기 위해 개발한 이전의 시역계측기술[1]은 기존의 비올라-존스 얼굴 검출기[2]에 의한 얼굴검출 결과와 비올라-존스 얼굴 검출기의 단점을 보완하기 위해 새롭게 추가된 옵티컬-플로우 특징점 추적 알고리즘[3]에 의한 얼굴검출의 두 결과를 선형적으로 결합하여 시청자의 시역위치를 예측하였다. 하지만, 모바일 3D 디스플레이의 특성한 급격한 조명의 변화에서 옵티컬-플로우에 의한 특징점 추적알고리즘에 심각한 오류가 발생하는 문제점이 있다. 이러한 급격한 조명의 변화에 대한 문제점을 해결하기 위해 본 논문에서는 매 프레임마다 정확하게 옵티컬-플로우 얼굴 검출기의 정확도를 판단할 수 있는 방법을 제안하고, 다양한 실험을 통해 그 효과를 검증한다.

• Smart Media Journal
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• v.5 no.1
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• pp.24-29
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• 2016

To satisfy the needs of high-level intelligent surveillance system, it shall be able to extract objects and classify to identify precise information on the object. The representative method to identify one's identity is face recognition that is caused a change in the recognition rate according to environmental factors such as illumination, background and angle of camera. In this paper, we analyze the robust face recognition of face image by changing the distance through a variety of experiments. The experiment was conducted by real face images of 1m to 5m. The method of face recognition based on Linear Discriminant Analysis show the best performance in average 75.4% when a large number of face images per one person is used for training. However, face recognition based on Convolution Neural Network show the best performance in average 69.8% when the number of face images per one person is less than five. In addition, rate of low resolution face recognition decrease rapidly when the size of the face image is smaller than $15{\times}15$.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.1
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• pp.196-204
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• 2008

In this paper, we propose an efficient method to classify human facial impression using frontal face image. The features that represent the shape of eye, jaw and face are used. The proposed method employs PCA, LDA and SVM in series. PCA is used to project the feature space to a low dimensional subspace. LDA produces well separated classes in a low dimensional subspace even under severe variation. This results in good discriminating power for classification. SVM is used to classify the data. Human face has been classified for 8 facial impressions. The experiments have been performed for many face images, and show encouraging result.