Journal of the Institute of Electronics Engineers of Korea CI (전자공학회논문지CI)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Robust feature vector composition for frontal face detection

노이즈에 강인한 정면 얼굴 검출을 위한 특성벡터 추출법

  • Lee Seung-Ik (School of Electronic Information and Communication Eng., Kyungil University) ;
  • Won Chulho (School of Electronic Information and Communication Eng., Kyungil University) ;
  • Im Sung-Woon (School of Electronic Information and Communication Eng., Kyungil University) ;
  • Kim Duk-Gyoo (School of Electrical Engineering & Computer Science, Kyungpook National University)
  • 이승익 (경일대학교 전자정보통신공학부) ;
  • 원철호 (경일대학교 전자정보통신공학부) ;
  • 임성운 (경일대학교 전자정보통신공학부) ;
  • 김덕규 (경북대학교 전자전기컴퓨터학부)
  • Published : 2005.11.01
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Abstract

The robust feature vector selection method for the multiple frontal face detection is proposed in this paper. The proposed feature vector for the training and classification are integrated by means, amplitude projections, and its 1D Harr wavelet of the input image. And the statistical modeling is performed both for face and nonface classes. Finally, the estimated probability density functions (PDFs) are applied for the detection of multiple frontal faces in the still image. The proposed method can handle multiple faces, partially occluded faces, and slightly posed-angle faces. And also the proposed method is very effective for low quality face images. Experimental results show that detection rate of the propose method is $98.3\%$ with three false detections on the testing data, SET3 which have 227 faces in 80 images.

본 논문에서는 정면 얼굴 검출에 이용되는 특성 벡터의 새로운 추출법을 제안한다. 새로운 특성벡터의 추출은 일차원 Harr 웨이블릿, 평균행렬, 분산행렬 및 진폭 투시법을 이용하여 각 각의 특성벡터를 구하였으며 얼굴 및 비 얼굴의 모델링은 확률적 특성을 이용한 조건부 확률 분포 함수로 모델링 한다. 또한 계산된 확률 분포 함수를 이용한 확률 값을 계산하여 입력 영상에서의 얼굴 검출을 수행한다. 제안한 방법으로 구성된 특성 벡터를 이용한 얼굴 검출에서는, 영상 내에서의 다수의 얼굴 검출이 가능하며 약간의 각도를 가지는 얼굴 검출도 가능하며 저해상도의 영상에서의 얼굴 검출에 매우 효과적이며 모의실험 결과 SET3의 테스트 영상에서의 얼굴 검출율은 $98.3\%$가 됨을 확인하였다.

Keywords

References

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