Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지

Face Recognition Robust to Local Distortion using Modified ICA Basis Images

개선된 ICA 기저영상을 이용한 국부적 왜곡에 강인한 얼굴인식

  • 김종선 (연세대학교 생체인식연구센터) ;
  • 이준호 (성균관대학교 정보통신공학부)
  • Published : 2006.05.01
Download PDF
⟨ Previous Next ⟩

Abstract

The performance of face recognition methods using subspace projection is directly related to the characteristics of their basis images, especially in the cases of local distortion or partial occlusion. In order for a subspace projection method to be robust to local distortion and partial occlusion, the basis images generated by the method should exhibit a part-based local representation. We propose an effective part-based local representation method named locally salient ICA (LS-ICA) method for face recognition that is robust to local distortion and partial occlusion. The LS-ICA method only employs locally salient information from important facial parts in order to maximize the benefit of applying the idea of 'recognition by parts.' It creates part-based local basis images by imposing additional localization constraint in the process of computing ICA architecture I basis images. We have contrasted the LS-ICA method with other part-based representations such as LNMF (Localized Non-negative Matrix Factorization) and LFA (Local Feature Analysis). Experimental results show that the LS-ICA method performs better than PCA, ICA architecture I, ICA architectureII, LFA, and LNMF methods, especially in the cases of partial occlusions and local distortions.

부공간 투영기술(subspace projection)을 이용한 얼굴인식기술의 성능은 이들 기저영상들(basis images)의 특징과 밀접한 관련이 있다. 특히 표정변화와 같은 국부적 왜곡이나 오클루전이 있는 경우의 인식성능은 기저영상들의 특징에 의해 영향을 받게 된다. 부공간 투영기반의 얼굴인식 방법이 오클루전이나 표정변화와 같은 국부적인 왜곡발생에 강인하려면 부분국부적 표현(part-based local representation)의 기저벡터를 갖는 것이 중요하다. 본 연구에서는 국부적 왜곡과 오클루전에 강인한 효과적인 부분국부적 표현방법을 제안한다. 제안한 방법을 LS-ICA(locally salient ICA) 방법이라고 명명하였다. LS-ICA방법은 ICA 구조I의 기저영상을 구하는 과정에서 공간적인 국부성(locality)의 제약조건을 부과함으로써 부분국부적 기저영상(part-based local basis images)을 얻는 방법이다. 결과적으로 공간적으로 현저한 특징만을 포함하는 기저영상을 사용하게 되며, 이는 "Recognition by Parts"의 방법론과 유사하다. LS-ICA방법과 LNMF(Localized Non-negative Matrix Factorization)와 LFA(Local Feature Analysis)와 같은 기존의 부분 표현방법(part-based representation)들에 대해 다양한 얼굴영상 데이타베이스를 사용하여 실험한 결과, LS-ICA방법이 기존의 방법에 비하여 높은 인식성능을 보였으며, 특히 오클루전이나 국부적인 변형이 포함된 얼굴영상에서 뛰어난 인식성능을 보였다.

Keywords

References

  1. M. A. Turk and A. P. Pentland, 'Eigenfaces for recognition,' Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991 https://doi.org/10.1162/jocn.1991.3.1.71
  2. M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, 'Face Recognition by Independent Component Analysis,' IEEE Trans. Neural Networks, vol. 13, no. 6, pp. 1450-1464, 2002 https://doi.org/10.1109/TNN.2002.804287
  3. Aapo Hyvarinen and Erki Oja, 'Independent component analysis: a tutorial,' http://www.cis.hut.fi/-aapo/papers/IJCNN99_tutorialweb/, 1999
  4. A. Hyvarinen, 'The Fixed-point Algorithm and Maximum Likelihood Estimation for Independent Component Analysis,' Neural Processing Letters, vol. 10, pp. 1-5, 1999 https://doi.org/10.1023/A:1018647011077
  5. P. Belhumeur, J. Hespanha, and D. Kriegman, 'Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection,' IEEE PAMI, vol. 19, no. 7, pp. 711-720, 1999 https://doi.org/10.1109/34.598228
  6. A. P. Pentland, 'Recognition by Parts,' IEEE Proceedings of the First International Conference on Computer Vision, pp. 612-620, 1987
  7. A. J. Bell and T. J. Sejnowski, 'The Independent Components of Natural Scenes are Edge Filters,' Vision Research, vol. 37, no. 23, pp. 3327-3338, 1997 https://doi.org/10.1016/S0042-6989(97)00121-1
  8. P. Penev and J. Atick, 'Local Feature Analysis: A general statistical theory for object representation,' Network: Computation in Neural Systems, vol. 7, no. 3, pp. 477-500, 1996 https://doi.org/10.1088/0954-898X/7/3/002
  9. S. Z. Li, X. W. Hou, H. J. Zhang, 'Learning Spatially Localized, Parts-Based Representation,' Computer Vision and Pattern Recognition, vol. 1, pp, 207-212, 2001 https://doi.org/10.1109/CVPR.2001.990477
  10. D. D. Lee and H. S. Seung, 'Learning the parts of objects by non-negative matrix factorization,' Nature, vol. 401, pp. 788-791, 1999 https://doi.org/10.1038/44565
  11. J. S. Kim, J. M. Choi, J. H. Yi, 'ICA Based Face Recognition Robust to Partial Occlusion and Local Distortions,' ICBA 2004, LNCS, vol. 3072, pp. 147-154, 2004
  12. P. J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss, 'The FERET Evaluation Methodology for Face Recognition Algorithms,' IEEE PAMI, vol. 22, no. 10, pp. 1090-1104, 2000 https://doi.org/10.1109/34.879790
  13. A. M. Martinez and R. Benavente, 'The AR face database,' CVC Tech, 1998
  14. http://www.uk.research.att.com/facedatabase.html
  15. B. A. Draper, K. Baek, M. S. Bartlett, and J. R. Beveridge, 'Recognizing faces with PCA and ICA,' Computer Vision and Image Understanding, vol. 91, no. 1, pp. 115-137, 2003 https://doi.org/10.1016/S1077-3142(03)00077-8
  16. M. S. Bartlett, Face Image Analysis by Unsupervised Learning, Kluwer Academic Publishers, 2001
  17. S. Wild, J. Curry, A. Dougherty, 'Motivating Non-Negative Matrix Factorizations,' In Proceedings of the Eighth SIAM Conference on Applied Linear Algebra, July 2003