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New Rectangle Feature Type Selection for Real-time Facial Expression Recognition

실시간 얼굴 표정 인식을 위한 새로운 사각 특징 형태 선택기법

  • 김도형 (한국과학기술원 전자전산학과) ;
  • 안광호 (한국과학기술원 전자전산학과) ;
  • 정명진 (한국과학기술원 전자전산학과) ;
  • 정성욱 (한국전자통신연구원)
  • Published : 2006.02.01

Abstract

In this paper, we propose a method of selecting new types of rectangle features that are suitable for facial expression recognition. The basic concept in this paper is similar to Viola's approach, which is used for face detection. Instead of previous Haar-like features we choose rectangle features for facial expression recognition among all possible rectangle types in a 3${\times}$3 matrix form using the AdaBoost algorithm. The facial expression recognition system constituted with the proposed rectangle features is also compared to that with previous rectangle features with regard to its capacity. The simulation and experimental results show that the proposed approach has better performance in facial expression recognition.

Keywords

References

  1. P. Ekman and W. V. Friesen 'Unmasking the face,' Malor Books press, 2003
  2. M. Pantie, L. J. M. Rothkrantz, 'Automatic analysis of facial expression: the state of art,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1424-1445, 2000 https://doi.org/10.1109/34.895976
  3. B. Fasel, J. Luettin, 'Automatic facial expression analysis: a survey,' Pattern Recognition, vol. 36, no. 1, pp. 259-275, 2003 https://doi.org/10.1016/S0031-3203(02)00052-3
  4. J. A. Essa, A. P. Pentland, 'Coding, analysis, interpretation, and recognition of facial expressions,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 757-763, 1997 https://doi.org/10.1109/34.598232
  5. A. Lanitis, C. J. Taylor, and T. F. Cootes, 'Automatic interpretation and coding of face images using flexible models,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol 19, no, 7, pp. 743-756, 1997 https://doi.org/10.1109/34.598231
  6. C. L. Huang and Y. M. Huang. 'Facial expression recognition using model-based feature extraction and action parameters calssification,' Journal of Visual Communication and Image Representation, vol. 8, no. 3, pp. 278-290, 1997 https://doi.org/10.1006/jvci.1997.0359
  7. Z. Zhang, M. Lyons, M. Schuster, and S. Akamatsu, 'Comparision between geometry-based and gabor-wavelets-based facial expression recognition using multi-layer perceptron,' The Second IEEE International Conference on Automatic Face and Gesture Recognition, pp. 454-459, 1998 https://doi.org/10.1109/AFGR.1998.670990
  8. C. L. Lisetti and D. E. Rumelhart, 'Facial expression recognition using a neural network,' The 11th International Flairs Conference, AAAI Press, 1998
  9. W. A. Fellenz, J. G. Taylor, N. Tsapatsoulis, and S. Kollias, 'Comparing template-based, feature-based and supervised classification of facial expressions form static images,' Circuits, Systems, Communications and Computers, pp. 5331-5336, 1999
  10. C. Padgett and G. W. Cottrell, 'Representing face image for emotion classification,' In M. Mozer, M. Jordan, and T. Petsche, editors, Advances in Neural Information Proceessing Systems, vol. 9, pp. 894-900, Cambridge, MA, 1997, MIT Press
  11. M. S. Bartlett, 'Face image analysis by unsupervised learning and redundancy reduction,' PhD thesis, Universiy of California, San Diego, 1998
  12. J. J. Lien, T. Kanade, J. F. Cohn, cc Li, 'Automated facial expression recognition based F ACS action units,' The Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 390-395, 1998 https://doi.org/10.1109/AFGR.1998.670980
  13. P. Viola and M. Jones, 'Rapid object detection using a boosted cascade of simple features,' IEEE International Conference on Computer Vision and Pattern Recognition, pp. 511-518, 2001 https://doi.org/10.1109/CVPR.2001.990517
  14. Y. Wang, H. Ai, B. Wu, C. Huang, 'Real time facial expression recognition with adaboost,' 17th IEEE International Conference on Pattern Recognition, vol. 3, pp. 926-929, 2004 https://doi.org/10.1109/ICPR.2004.1334680
  15. G. C. Littlewort, M. S. Bartlett, J. Chenu, I. Fasel, T. Kanda, H. Ishiguro, & J. R. Movellan, 'Towards social robots: automatic evaluation of human-robot interaction by face detection and expression classification,' In S. Thrun & L. Saul & B. Schoelkopf, (Eds.) Advances in Neural Information Processing Systems, vol 16. pp. 1563-1570, MIT Press, 2004
  16. The Japanese Female Facial Expression(JAFFE) Database, http://www.irc.atr.jp/~mlyons/jaffe.htm