전자공학회논문지 (Journal of the Institute of Electronics and Information Engineers)

  • 제51권11호
  • /
  • Pages.193-201
  • /
  • 2014
  • /
  • 2287-5026(pISSN)
  • /
  • 2288-159X(eISSN)
대한전자공학회 (The Institute of Electronics and Information Engineers)
권호 표지
DOI QR코드

DOI QR Code

얼굴정렬과 AdaBoost를 이용한 얼굴 표정 인식

Facial Expression Recognition using Face Alignment and AdaBoost

  • 정경중 (울산과학기술대학교 전기전자컴퓨터공학부) ;
  • 최재식 (울산과학기술대학교 전기전자컴퓨터공학부) ;
  • 장길진 (경북대학교 전자공학부)
  • Jeong, Kyungjoong (School of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology) ;
  • Choi, Jaesik (School of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology) ;
  • Jang, Gil-Jin (School of Electronics Engineering, Kyungpook National University)
  • 투고 : 2014.08.22
  • 심사 : 2014.10.30
  • 발행 : 2014.11.25
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 얼굴영상에 나타난 사람의 표정을 인식하기 위해 얼굴검출, 얼굴정렬, 얼굴단위 추출, 그리고 AdaBoost를 이용한 학습 방법과 효과적인 인식방법을 제안한다. 입력영상에서 얼굴 영역을 찾기 위해서 얼굴검출을 수행하고, 검출된 얼굴영상에 대하여 학습된 얼굴모델과 정렬(Face Alignment)을 수행한 후, 얼굴의 표정을 나타내는 단위요소(Facial Units)들을 추출한다. 본 논문에서 제안하는 얼굴 단위요소들을 표정을 표현하기 위한 기본적인 액션유닛(AU, Action Units)의 하위집합으로 눈썹, 눈, 코, 입 부분으로 나눠지며, 이러한 액션유닛에 대하여 AdaBoost 학습을 수행하여 표정을 인식한다. 얼굴유닛은 얼굴표정을 더욱 효율적으로 표현할 수 있고 학습 및 테스트에서 동작하는 시간을 줄여주기 때문에 실시간 응용분야에 적용하기 적합하다. 실험결과, 제안하는 표정인식 시스템은 실시간 환경에서 90% 이상의 우수한 성능을 보여준다.

This paper suggests a facial expression recognition system using face detection, face alignment, facial unit extraction, and training and testing algorithms based on AdaBoost classifiers. First, we find face region by a face detector. From the results, face alignment algorithm extracts feature points. The facial units are from a subset of action units generated by combining the obtained feature points. The facial units are generally more effective for smaller-sized databases, and are able to represent the facial expressions more efficiently and reduce the computation time, and hence can be applied to real-time scenarios. Experimental results in real scenarios showed that the proposed system has an excellent performance over 90% recognition rates.

키워드

참고문헌

  1. P. Ekman and W. V. Friesen, "The Facial Action Coding System: A Technique For The Measurement of Facial Movement," Consulting Psychologists Press Inc., San Francisco, CA, 1978.
  2. Y. Tian, T. Kanade, and J. Cohn, "Recognizing action units for facial expression analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 97 - 115, Feb. 2001. https://doi.org/10.1109/34.908962
  3. H. Sadeghi, A. Raie, and M. Mohammadi, "Facial Expression Recognition using Geometric Normalization and Appearance Representation," 2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP), pp. 10-12, Sept. 2013
  4. D.J. Kim, M.K. Sohn, and S.H. Lee, "A Study on Face Recognition Method based on Binary Pattern Image under Varying Lighting Condition," The Institute of Electronics Engineers of Korea - Computer and Information, Vol.49-CI, No.2, pp. 61-74, 2012.
  5. E. Osuna, Support Vector Machines: Training and Applications, PhD thesis, MIT, EE/CS Dept., Cambridge, MA, 1998.
  6. A. Mohan, C. Papageorgiou, and T. Poggio, "Example-based object detection in images by components," IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(4):349-361, 2001. https://doi.org/10.1109/34.917571
  7. I. Kotsia, I. Buciu and I. Pitas, "An Analysis of Facial Expression Recognition under Partial Facial Image Occlusion," Image and Vision Computing, vol. 26, no. 7, pp. 1052-1067, 2008. https://doi.org/10.1016/j.imavis.2007.11.004
  8. J.H. Kim, K.H. Jang, J.H. Lee, and Y.S. Moon, "Multi-target Classification Method Based on Adaboost and Radial Basis Function," The Institute of Electronics Engineers of Korea - Computer and Information, Vol.47-CI, No.3, pp. 22-28, 2010.
  9. P. Viola and M. Jones, "Fast and Robust Classification using Asymmetric Adaboost and a Detector Cascade," Advances in Neural Information Processing System 14, MIT Press, Cambridge, MA, 2002.
  10. B. Froba and A. Ernst, "Face Detection with the Modified Census Transform," in Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition (FGR'04), 91-96, 2004.
  11. X. Cao, Y. Wei, F. Wen, and J. Sun, "Face alignment by explicit shape regression," in Proceedings of the IEEE international Conference of Computer Vision and Pattern Recognition (CVPR), 2012.
  12. S.J. Lim, Y.Y. Jeong, and Y.S. Ho, "Three-dimensional Model Generation for Active Shape Model Algorithm," The Institute of Electronics Engineers of Korea - Signal Processing, Vol.43, No.6, pp 28-35, November 2006.
  13. J.Y. Kang, S.W. Lee, J.H. Shin, and J.K. Paik, "Hierarchical Active Shape Model-based Motion Estimation for Real-time Tracking of Non-rigid Object," The Institute of Electronics Engineers of Korea - Signal Processing, Vol.41, No.5, pp. 1-11, September 2004.
  14. H.J. Park, J.H. Lee, T.H. Kim, and E.Y. Cha, "Multiple Active Appearance Model Based Facial Expression Recognition," in Proceedings of the Institute of Electronics Engineers of Korea, pp. 2138-2141, June 2010.