DOI QR코드

DOI QR Code

Face Detection using Brightness Distribution in the Surrounding Area of Eye

눈 주변영역의 명암분포를 이용한 얼굴탐지

  • 황대동 (숭실대학교 컴퓨터학과) ;
  • 박주철 (배화여자대학 컴퓨터정보과) ;
  • 김계영 (숭실대학교 컴퓨터학과)
  • Published : 2009.12.31

Abstract

This paper develops a novel technique of face detection using brightness distribution in the surrounding area of eye. The proposed face detection consists of facial component candidate extraction, facial component candidate filtering through eye-lip combination, left/right eye classification using brightness distribution, face verification confirming edges in nose region. Because the proposed technique don't use any skin color, it can detect multiple faces in color images with complicated backgrounds and different illumination levels. The experimental results reveal that the proposed technique is better than the traditional techniques in terms of detection ratio.

본 논문에서는 눈 주변의 명암분포를 사용하여 영상에 존재하는 얼굴을 탐지하는 새로운 기술을 개발한다. 제안하는 얼굴탐지의 기본적인 절차는 얼굴구성요소 후보 추출, 눈과 입의 형태정보를 이용한 얼굴구성요소 후보 필터링, 눈 후보 주변영역의 에지와 명암분포를 인공신경망 에 적용하여 좌/우안 분류, 눈-입 조합을 통한 얼굴후보 추출, 코 영역 에지의 존재 유무를 이용한 얼굴 검증 순이다. 본 논문에서 제안하는 방식은 눈의 주변영역 정보를 인공신경망에 적용하여 좌/우안 정보를 산출하여 얼굴을 탐지하는 것에 중점을 두고 있다. 이 방법은 피부색상을 이용하지 않으므로 다양한 조명환경과 복잡한 배경을 가지는 영상들에 존재하는 얼굴을 탐지할 수 있다. 탐지율 관점에서 기존의 주요 방법들 보다 우수함을 실험을 통하여 보인다.

Keywords

References

  1. T. Kawaguchi, and M. Rizon, “Iris detection using intensity and edge information,” Pattern Recognition, Vol.36, No.22, pp.549-562, 2003 https://doi.org/10.1016/S0031-3203(02)00066-3
  2. J. Song, Z. Chi, and J. Liu, “A robust eye detection method using combined binary edge and intensity information,” Pattern Recognition, Vol.39, No.6, pp.1110-1125, 2006 https://doi.org/10.1016/j.patcog.2005.11.015
  3. R. Brunelli, and T. Poggio, “Face recognition: features versus templates,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.15, No.10, pp.1042-1052, 1993 https://doi.org/10.1109/34.254061
  4. A. Pentland, B. Moghaddam, and Thad Starner, “View-based and modular eigenspaces for face recognition,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp.84-91, 1994
  5. P. Viola, and M. Jones, “Rapid object detection using a boosted cascade of simple features,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp.511-518, 2001
  6. B. Fr$\ddot{o}$ba, and A. Ernst, “Face- Detection with the Modified Census Transform”, In Proceedings of the IEEE Conference on Automatic Face and Gesture Recognition, pp.91-96, 2004
  7. R.L. Hsu, M. Abdel-Mottaleb, “Face Detection in Color Images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.24, No.5, pp.696-706, 2002 https://doi.org/10.1109/34.1000242
  8. P. T. Jackway, “Scale-Space Properties of the Multiscale Morphological Dilation-Erosion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.18, No.1, pp.38-51, 1996 https://doi.org/10.1109/34.476009
  9. C. Lin, K.C. Fan, “Triangle-based approach to the detection of human face,” Pattern Recognition Society, Vol.34, pp. 1271-1284, 2001 https://doi.org/10.1016/S0031-3203(00)00075-3
  10. B. Heisele, T. Serre, M. Pontil, T. Poggio, “Component-based face detection,” IEEE Conf. on Computer Vision and Pattern Recognition, Vol.1, pp.657-662, 2001
  11. M. Abdel-Mottaleb, A. Elgammal, “Face Detection in Complex Environments from Color Images,” IEEE Conf. Image Processing. pp.622-626, 1999
  12. J. Shih, C. Lee, and C. Yang, “An Adult Image Identification System Employing Image Retrieval Technique,” Pattern Recognition Letters, Vol.28, pp.2367-2374, 2007 https://doi.org/10.1016/j.patrec.2007.08.002
  13. K. M. Lee, “Component-based detection and verification,” Pattern Recognition Letters, Vol.29, pp.200-214, 2008 https://doi.org/10.1016/j.patrec.2007.09.013