Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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An Improved LBP-based Facial Expression Recognition through Optimization of Block Weights

블록가중치의 최적화를 통해 개선된 LBP기반의 표정인식

  • Published : 2009.11.30
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Abstract

In this paper, a method is proposed that enhances the performance of the facial expression recognition using template matching of Local Binary Pattern(LBP) histogram. In this method, the face image is segmented into blocks, and the LBP histogram is constructed to be used as the feature of the block. Block dissimilarity is calculated between a block of input image and the corresponding block of the model image. Image dissimilarity is defined as the weighted sum of the block dissimilarities. In conventional methods, the block weights are assigned by intuition. In this paper a new method is proposed that optimizes the weights from training samples. An experiment shows the recognition rate is enhanced by the proposed method.

본 논문에서는 Local Binary Pattern 히스토그램의 템플릿 매칭을 이용한 얼굴 표정 인식에서 인식률을 높이는 방법을 제안한다. 이 방법에서, 주어진 얼굴 영상은 작은 크기의 블록으로 분할되고 각 블록에서 구해진 LBP 히스토그램은 블록 특징으로 사용된다. 입력 영상에서의 블록 특징과 모델의 해당블록 특징 사이에서 블록 상이도가 계산된다. 주어진 영상과 모델 영상 사이의 영상 상이도는 블록 상이도의 가중 합으로 계산된다. 기존의 방법들에서는 직관에 따른 블록 가중치를 사용하는데 본 논문에서는 블록 가중치를 트레이닝 샘플들로부터 최적화를 통해서 구하는 방법을 제안하고 있다. 실험을 통해서 제안된 방법이 기존의 방법보다 우수함을 보인다.

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References

  1. Y. Tian, L. Brown, A. Hampapur, S. Pankanti, A. Senior, and R. Bolle, "Real world real time automatic recognition of facial expression," IEEE PETS, Australia, Mar. 2003.
  2. B. Fasel and J. Luettin. "Automatic facial expression analysis: a survey," Pattern Recognition, Vol. 36, pp, 259 - 275, 2003. https://doi.org/10.1016/S0031-3203(02)00052-3
  3. M. J. Lyons, J. Budynek, and S. Akamatsu, "Automatic classification of single facial images," IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999.
  4. I. Cohen, N. Sebe, Garg A., L. Chen, and T. Huang, "Facial expression recognition from video sequences: Temporal and static modeling," CVIU, Vol. 91, pp. 160 -187, 2003.
  5. M.S. Bartlett, G. Littlewort, 1. Fasel, and R. Movellan, "Real time face detection and facial expression recognition: Development and application to human computer interaction," CVPR Workshop on CVPR for HCI. 2003.
  6. Y. Tian, "Evaluation of face resolution for expression analysis," IEEE Workshop on Face Processing in Video, 2004.
  7. Y. Tian, T. Kanade, and J.F. Cohn, "Facial Expression Analysis," Handbook of Face Recognition, Springer, Oct. 2003.
  8. M. Lyons, S. Akamatsu, M. Karnachi, J. Gyoba, "Coding Facial Expressions with Gabor Wavelets," pp.200, Third IEEE International Conference on Automatic Face and Gesture Recognition (FG'98), 1998.
  9. T. Ahonen, A. Hadid, and M. Pietikinen, "Face recognition with local binary patterns," ECCV, pp. 469 - 481, 2004.
  10. A. Hadid, M. Pietikinen, and T. Ahonen, "A discriminative feature space for detecting and recognizing faces," IEEE CVPR, pp. 797 - 804. June 2004.
  11. C. Shan, S. Gong, and P. W. McOwan. "Robust facial expression recognition using local binary patterns," IEEE ICIP, 2005.
  12. T. Ojala, M Pietikinen. and D. Harwood "A comparative study of texture measures with classification based on featured distribution," Pattern Recognition, Vol. 29, No. 1, 1996.
  13. T Oiala, M Pietikainen. T Maenpaa "Multireeolution gray scale and rotation invariant texture classification with local binary patterns" IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 971-987, 2002.
  14. M.J. Lyons, J. Budynek, S. Akamatsu, "Automatic classification of single facial images," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 12, PP.757-1362, 1999.
  15. 정성태 "조명변화에 견고한 얼굴 특정 추출" 한국컴퓨터 정보학회논문지, 제 10권, 제 6호. 1-8쪽. 2005년 12월.
  16. 김진용, 유재휘 "아바타 통신에서의 얼굴 표정의 생성 방법" 한국컴퓨터정보학회논문지 , 제 10권, 제 3호. 55-64 쪽.2005년 7월.