Robust Facial Expression-Recognition Against Various Expression Intensity

표정 강도에 강건한 얼굴 표정 인식

  • 김진옥 (대구한의대학교 모바일콘텐츠학부)
  • Published : 2009.10.31


This paper proposes an approach of a novel facial expression recognition to deal with different intensities to improve a performance of a facial expression recognition. Various expressions and intensities of each person make an affect to decrease the performance of the facial expression recognition. The effect of different intensities of facial expression has been seldom focused on. In this paper, a face expression template and an expression-intensity distribution model are introduced to recognize different facial expression intensities. These techniques, facial expression template and expression-intensity distribution model contribute to improve the performance of facial expression recognition by describing how the shift between multiple interest points in the vicinity of facial parts and facial parts varies for different facial expressions and its intensities. The proposed method has the distinct advantage that facial expression recognition with different intensities can be very easily performed with a simple calibration on video sequences as well as still images. Experimental results show a robustness that the method can recognize facial expression with weak intensities.

본 연구는 표정 인식률을 개선하기 위한, 강도가 다른 표정을 인식하는 새로운 표정 인식 방법을 제안한다. 사람마다 다르게 나타나는 표정과 표정마다 다른 강도는 표정 인식률 저하에 지대한 영향을 미친다. 하지만 얼굴 표정의 다양한 강도를 처리하는 방법은 많이 제시되지 않고 있다. 본 연구에서는 표정 템플릿과 표정 강도 분포모델을 이용하여 다양한 얼굴 표정 강도를 인식하는 방법을 제시한다. 표정 템플릿과 표정강도 분포모델은 얼굴의 특징 부위에 표시한 관심 점과 얼굴 특징 부위간의 움직임이 다른 표정과 강도에 따라 어떻게 달라지는지 설명하여 표정 인식률 개선에 기여한다. 제안 방법은 정지 이미지뿐만 아니라 비디오시퀀스에서도 빠른 측정 과정을 통해 다양한 강도의 표정을 인식할 수 있는 장점이 있다. 실험 결과, 제안 연구가 특히 약한 강도의 표정에 대해 타 방법보다 높은 인식 결과를 보여 제안 방법이 다양한 강도의 표정 인식에 강건함을 알 수 있다.



  1. Paul Ekman, Wallace Friesen, Joseph Hager, "Facial Action Coding System Manual," 2002.
  2. M. A. Sayette, J. F. Cohn, J. M. Wertz, M. Perrott, D. Parrott, "Psychometric Evaluation of the Facial Action Coding System for Assessing Spontaneous Expression," Journal of Nonverbal Behavior, vol. 25. no. 3, pp. 167-185, Springer, 2001.
  3. Mohammed Yeasin, Baptiste Bullot, Rajeev Sharma, "Recognition of Facial Expressions and Measurement of Levels of Interest from Video," IEEE Transactions on Multimedia, Vol.8, No.3, 2006.
  4. S. Kimura, M. Yachida, "Facial Expression Recognition and its Degree Eestimation," International Conference on Computer Vision and Pattern Recognition, pp.295-300, 1997.
  5. J. J. Lien, T. Kanade, J. Cohn, C. Li, "Subtly different facial expression recognition and expression intensity estimation," Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp.853-859, 1998.
  6. M. Barelett, G. Littlewort, M. Franc, C. Ainscsek, I. Fasel, J. Movellan, "Automatic Recognition of Facial Actions in Spontaneous Expressions," Journal of Multimedia, Vol.1, No.6, pp.22-35, 2006.
  7. Y. L. Tian, T. Kanade, J. Cohn, "Eye-state Aciton Unit Detection by Gabor Wavelets," Proceedings of International Conference on Multi-Model Interfaces, pp.143-150, 2000.
  8. J. Wang, L. Yin, X. Wei, Y. Sun, "3D Facial Expression Recognition based on Primitive Surface Feature Distribution," IEEE Computer Vision and Pattern Recognition, pp.1399- 1406, 2006.
  9. F. Drnaika, F. Davoine, "Simultaneous Facial Action Tracking and Expression Recognition in the presence of head motion," Int. Journal of Computer Vision, Vol.76, No.3, pp.257-281, 2008.
  10. S. Lucey, I. Mathews, C. Hu, Z, Ambadar, F. Torre, J. Cohn, "AAM Derived Face Representations for Robust Facial Action Recognition," IEEE Int. Conf. Automatic Face and Gesture Recognition, pp.155-160, 2006.
  11. Jane Reilly, John Ghent, John Mcdonald, "Investigating the Dynamics of Facial Expressions," Lecture Notes in Computer Science, Vol.4292, pp.334-343, 2006.
  13. J. O. Kim, J. H. Chung, "On a Face Detection with an Adaptive Template Matching and an Efficient Cascaded Object Detection," Lecture Notes in Computer Science, Vol.3645, pp.414-422, 2005.
  14. M. Isard, A. Blake, " Condensation-conditional density propagation for visual tracking", International Journal of Computer Vision, Vol.29, No.1, pp.5-28, 1998
  15. R. Gonzalez, R. Woods, "Digital Image Processing", Addison-Wesley Publishing Company, pp.142, 1992.
  16. M. S. Bartlett, "Machine Learning Methods for Fully Automatic Recognition of Facial Expressions and Actions," Proc. IEEE Conf. of SMC, pp.592-597, 2004.
  17. M. Black, A. Rangarajan, "On the Unification of Line Processes, Outlier Rejection, and Robust Statistics with Applications in Early Vision," International Journal of Computer Vision, Vol.19, No.1, pp.57-92, 1996.
  18. 김진옥, "표정 정규화를 통한 얼굴 인식율 개선", 정보처리학회 논문지B, 제15-B권, pp.477-486, 2008.
  19. 김진옥,"상황에 민감한 베이지안 분류기를 이용한 얼굴 표정 기반의 감정 인식", 정보처리학회논문지B, 제13-B권, pp.653- 662, 2006.

Cited by

  1. Impact Analysis of nonverbal multimodals for recognition of emotion expressed virtual humans vol.13, pp.5, 2012,