Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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A flexible Feature Matching for Automatic Face and Facial Feature Points Detection

얼굴과 얼굴 특징점 자동 검출을 위한 탄력적 특징 정합

  • Published : 2003.08.01
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Abstract

An automatic face and facial feature points(FFPs) detection system is proposed. A face is represented as a graph where the nodes are placed at facial feature points(FFPs) labeled by their Gabor features and the edges are describes their spatial relations. An innovative flexible feature matching is proposed to perform features correspondence between models and the input image. This matching model works likes random diffusion process in !be image space by employing the locally competitive and globally corporative mechanism. The system works nicely on the face images under complicated background, pose variations and distorted by facial accessories. We demonstrate the benefits of our approach by its implementation on the face identification system.

본 논문에서는 자동적으로 얼굴과 얼굴 특징점(FFPs:Facial Feature Points)을 검출하는 시스템을 제안하였다. 얼굴은 Gabor 특징에 의하여 지정된 특징점의 교점 그래프와 공간적 연결을 나타내는 에지 그래프로 표현하였으며 제안된 탄력적 특징 정합은 모델과 입력 영상에 상응하는 특징을 취하였다. 또한, 정합 모델은 국부적으로 경쟁적이고 전체적으로 협력적인 구조를 이룸으로서 영상공간에서 불규칙 확산 처리와 같은 역할을 하도록 하였으며, 복잡한 배경이나 자세의 변화, 그리고 왜곡된 얼굴 영상에서도 원활하게 동작하는 얼굴 식별 시스템을 구성함으로서 제안된 방법의 효율성을 증명하였다.

Keywords

References

  1. H. Wu, T. Yokoyama, D.Pramadihanto, and M.Yachida. Face and facial feature extraction from color image. Proc. of the Int. Worksh. on Autom. Face-and Gesture Recogn., 1996
  2. J.Daugman. Complete discrete 2-d gabor transform by neural networks for image analysis and compression. IEEE Trans. on Acoust., Speech, Signal Process., 36(7): 1169-1179, 1988 https://doi.org/10.1109/29.1644
  3. J.P.Jones and L.A.Palmer. An evaluation of the two-dimensional gabor filter model of simple receptive fields in cat striate cortex. Jour. of Neurophys., 58(6):1233-1258, 1987
  4. L.wiskott, J.M.Fellous, N.Kruger, and C. der Malsburg. Face recognition and gender determination.Proc.of the Int. Work. on Autom Face-and Gesture Recogn., pages 92-97, 1995
  5. M.Kass, A.P.witkin, and D.Terzopoulos. Snakes: Active contour models. Int. Jour. of Computer Vision, pages 321-331, 1988
  6. M.Lades, J.C.Vorbruggen, J.C. Buhmannm, R. C. von der Malsburg, and W.Konen. Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. on Computers, 42(6):300-311, 1993 https://doi.org/10.1109/12.210173
  7. R.Brunelli and T.Poggio. Face recognition: Features versus templates. IEEE Trans. on Pattern Analysis and Machine Intelligence, 15(10): 10 42-1052, 1993 https://doi.org/10.1109/34.254061
  8. Madhusudhana Gargesha, Sethuraman Panchanathan 'A Hybrid Technique for Facial Feature Point Detection', Fifth IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 0134-0138, April 2002
  9. R.S.Feris, T.E. de Campos, and R.M Cesar Junior, 'Detection and tracking of facial features in video sequences', Lecture Notes in Artificial Intelligence, vol. 1793, pp. 127-135, April 2000
  10. R.L.Hsu, M. Abdel-Mottaleb, and A.K. Jain, 'Face Detection in Color Images.' Proceedings of the IEEE International Conference on Image Processing, vol. 1, pp. 1046-1049. 2001
  11. Z. Xue, S.Z. U, and E.K. Teoh, 'Facial Feature extraction and image warping using PCA based statistic model.', International Conference on Image Processing , vol. 2, pp. 689-692. Oct. 2001
  12. Jian Huang Lai, Pong C Yuen, WenSheng Chen, Shihong Lao, Masato Kawade 'Robust Facial Feature Point Detection Under Nonlinear Illuminations' IEEE ICCV Workshop on RATFG-RTS'01 pp. 0168-0174, July 2001
  13. G. C. Feng and P. C. Yuen, 'Multi-cues eye detection on gray intensity image', To appear in Pattern Recognition, May 2001