운전자 피로 감지를 위한 얼굴 동작 인식

Facial Behavior Recognition for Driver's Fatigue Detection

  • 박호식 (오산대학 디지털전자과) ;
  • 배철수 (관동대학교 전자통신공학과)
  • 투고 : 2010.07.13
  • 심사 : 2010.08.02
  • 발행 : 2010.09.30

초록

본 논문에서는 운전자 피로 감지를 위한 얼굴 동작을 효과적으로 인식하는 방법을 제안하고자 한다. 얼굴 동작은 얼굴 표정, 얼굴 자세, 시선, 주름 같은 얼굴 특징으로 나타난다. 그러나 얼굴 특징으로 하나의 동작 상태를 뚜렷이 구분한다는 것은 대단히 어려운 문제이다. 왜냐하면 사람의 동작은 복합적이며 그 동작을 표현하는 얼굴은 충분한 정보를 제공하기에는 모호성을 갖기 때문이다. 제안된 얼굴 동작 인식 시스템은 먼저 적외선 카메라로 눈 검출, 머리 방향 추정, 머리 움직임 추정, 얼굴 추적과 주름 검출과 같은 얼굴 특징 등을 감지하고 획득한 특징을 FACS의 AU로 나타낸다. 획득한 AU를 근간으로 동적 베이지안 네트워크를 통하여 각 상태가 일어날 확률을 추론한다.

This paper is proposed to an novel facial behavior recognition system for driver's fatigue detection. Facial behavior is shown in various facial feature such as head expression, head pose, gaze, wrinkles. But it is very difficult to clearly discriminate a certain behavior by the obtained facial feature. Because, the behavior of a person is complicated and the face representing behavior is vague in providing enough information. The proposed system for facial behavior recognition first performs detection facial feature such as eye tracking, facial feature tracking, furrow detection, head orientation estimation, head motion detection and indicates the obtained feature by AU of FACS. On the basis of the obtained AU, it infers probability each state occur through Bayesian network.

키워드

참고문헌

  1. Yeenchena Lee, Demetri Terzopoulos and Keith Waters, "Constructing Physics-Based Facial Models of Indiciduals", Fraphics Interface '93, pp.1-8, 1993.
  2. Demetri Terzopoulos and Keith Waters, "Analysis and Anatomical Models", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.15, No.6, pp.569-579, 1993. https://doi.org/10.1109/34.216726
  3. C. Morimoto, M. Flickner, Real-time multiple face detection using active illumination, in: Proc. Fourth IEEE Internat. Conf. on Automatic face and Gesture Recognition, pp.8-13, 2000.
  4. L. Wiskott, J. Fellous, N. Kr ger, C.V. der Malsburg, Face recognition by elastic bunch graph matching, in: IEEE Trans. on Pattern Analysis Machine Intelligence, pp.775-779. 1997.
  5. P. Ekman, W.V. Friesen, J.C. Hager, Facial Action Coding System (FACS): Manual, CD Rom, San Francisco, CA, 2002.
  6. Vazquez. R.A, Sossa. H, Behavior of morphological associative memories with true-color image patterns, Neurocomputing, Vol.73, No.1, pp.225-244. 2009. https://doi.org/10.1016/j.neucom.2009.09.004
  7. Wang. T. H, James Lien. J. J, Facial expression recognition system based on rigid and non-rigid motion separation and 3D pose estimation, Pattern recognition, Vol.42 No.5, pp.962-977, 2009. https://doi.org/10.1016/j.patcog.2008.09.035
  8. Whitehill. J, Omlin. C.W, Local versus Global Segmentation for Facial Expression Recognition Automatic Face and Gesture Recognition, FGR 2006. 7th Intemational Conference on 2006, pp.357-362. 2006.
  9. Ksantini. R, Boufama. B, Ziou. D, Colin. B, A novel Bayesian logistic discriminant model: An application to face recognition, Pattern recognition, v.43, no.4, pp.1421-1430, 2010. https://doi.org/10.1016/j.patcog.2009.08.021
  10. Heusch. G, Marcel. S, A novel statistical generative model dedicated to face recognition, Image and vision computing, Vol.28, No.1, pp.101-110, 2010. https://doi.org/10.1016/j.imavis.2009.05.001
  11. I. Cohen, N. Sebe, F. Cozman, M. Cirelo, T. Huang. Learning bayesian network classi.ers for facial expression recognition using both labeled and unlabeled data. IEEE Conf. Computer Vision Pattern Recognition, 2003.
  12. I. Cohen, N. Sebe, A. Garg, L.S. Chen, T.S. Huang, Facial expression recognition from video sequences: temporal and static modeling, Comput. Vis. Image Und. 911, pp.60-187, 2003.
  13. R. Kaliouby, P. Robinson. Real-time inference of complex mental states from facial expressions and head gestures, in: Proc, in CVPR Workshop on Real-Time Computer Vision for Human Computer Interaction, p.154, 2004.
  14. N. Oliver, E. Horvitz, A. Garg. Layered representations for human activity recognition, in: Proc. IEEE Internat. Conf. on Multimodal Interfaces, pp.3-8, 2002.