• 한국통신학회논문지
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• 제29권6C호
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• pp.815-823
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• 2004

색상(hue) 기반 주목연산자와 조합누적투영함수(combinational integral projection function: CIPF)를 제안하여 조명변화에 강건하게 정규화된 얼굴요소영역을 추출하였다. 살색 필터를 도입하여 얼굴후보영역들을 추출하고, 거기에 색상과 대칭성에 기반한 주목연산자를 적용하여 조명변화에 강건하게 두 눈의 위치를 정확히 검출할 수 있도록 하였으며, 색상기반 눈 분산 필터로 눈을 검증하여 얼굴영역을 확인하였다. 또한, 색상과 밝기 성분을 조합한 조합누적투영함수를 사용하여 두 눈의 위치를 기준으로 조명변화나 수염의 존재유무에 둔감하게 눈썹 및 입의 수직위치를 구하고, 이를 바탕으로 정규화된 얼굴영역 및 그 요소영역을 추출하였다. AR 얼굴 데이터베이스［8］에 제안한 색상기반 주목연산자를 적용한 결과 기존 명도기반 주목연산자에 비해 약 39.3%의 눈 검출 성능향상을 보임으로써 조명방향 변화에 강건하게 정규화된 얼굴 및 그 요소영역을 일관성 있게 추출할 수 있음을 확인하였다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.299-302
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• 2001

The statistical analysis of the feature extraction and the neural networks are proposed to recognize a human face. In the preprocessing step, the normalized skin color map with Gaussian functions is employed to extract the region of face candidate. The feature information in the region of the face candidate is used to detect the face region. In the recognition step, as a tested, the 120 images of 10 persons are trained by the backpropagation algorithm. The images of each person are obtained from the various direction, pose, and facial expression. Input variables of the neural networks are the geometrical feature information and the feature information that comes from the eigenface spaces. The simulation results of$.$10 persons show that the proposed method yields high recognition rates.

• 한국지능시스템학회논문지
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• 제11권9호
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• pp.822-826
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• 2001

The statistical analysis of the feature extraction and the neural networks are proposed to recognize a human face. In the preprocessing step, the normalized skin color map with Gaussian functions is employed to extract the region of face candidate. The feature information in the region of the face candidate is used to detect the face region. In the recognition step, as a tested, the 120 images of 10 persons are trained by the backpropagation algorithm. The images of each person are obtained from the various direction, pose, and facial expression. Input variables of the neural networks are the geometrical feature information and the feature information that comes from the eigenface spaces. The simulation results of 10 persons show that the proposed method yields high recognition rates.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.55.2-55
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• 2001

The statistical analysis of the feature extraction and the neural networks are proposed to recognize a human face. In the preprocessing step, the normalized skin color map with Gaussian functions is employed to extract the region efface candidate. The feature information in the region of face candidate is used to detect a face region. In the recognition step, as a tested, the 360 images of 30 persons are trained by the backpropagation algorithm. The images of each person are obtained from the various direction, pose, and facial expression, Input variables of the neural networks are the feature information that comes from the eigenface spaces. The simulation results of 30 persons show that the proposed method yields high recognition rates.