The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Elongated Radial Basis Function for Nonlinear Representation of Face Data

  • 김상기 (연세대학교 전기전자공학과 영상인식 연구실) ;
  • 유선진 (LG전자 전자기술원/미래IT 융합 연구소) ;
  • 이상윤 (연세대학교 전기전자공학과 영상인식 연구실)
  • Received : 2011.06.03
  • Accepted : 2011.06.22
  • Published : 2011.07.30
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Abstract

Recently, subspace analysis has raised its performance to a higher level through the adoption of kernel-based nonlinearity. Especially, the radial basis function, based on its nonparametric nature, has shown promising results in face recognition. However, due to the endemic small sample size problem of face data, the conventional kernel-based feature extraction methods have difficulty in data representation. In this paper, we introduce a novel variant of the RBF kernel to alleviate this problem. By adopting the concept of the nearest feature line classifier, we show both effectiveness and generalizability of the proposed method, particularly regarding the small sample size issue.

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References

  1. W. Zhao, R. Chellappa, A. Rosenfeld, P.J. Phillips, "Face Recognition: A Literature Survey," ACM Computing Surveys, Vol.35, No.4, 2003, pp.399-458. https://doi.org/10.1145/954339.954342
  2. L. Torres, "Is there any hope for face recognition?," Proc. of the 5th International Workshop on Image Analysis for Multimedia Interactive Services, Lisbon, Portugal, Apr. 2004, pp.21-23.
  3. M. Turk and A. Pentland, "Eigenfaces for Recognition," Cognitive Neuroscience, Vol.3, No.1, pp.72-86, 1991.
  4. P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman, "Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection," IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.19, No.7, May 1997, pp.711-720. https://doi.org/10.1109/34.598228
  5. M. S. Bartlett, J.R. Movellan, and T.J. Sejnowski, "Face Recognition by Independent Component Analysis," IEEE Transaction on Neural Networks, Vol.13, No.6, Nov. 2002, pp.1450-1464. https://doi.org/10.1109/TNN.2002.804287
  6. C.J.C. Burges. "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery, Vol.2, No.2, 1998, pp. 121-167. https://doi.org/10.1023/A:1009715923555
  7. K.I. Kim, K. Jung, and H.J. Kim, "Face Recognition using Kernel Principal Component Analysis," IEEE Signal Processing Letters, Vol.9, No.2, Feb. 2002, pp.40-42. https://doi.org/10.1109/97.991133
  8. J. Ma, J.L. Sancho-Gomez, and S.C. Ahalt, "Nonlinear Multiclass Discriminant Analysis," IEEE Signal Processing Letters, Vol.10, No.7, Jul. 2003, pp.196-199. https://doi.org/10.1109/LSP.2003.813680
  9. J. Yang, X. Gao, D. Zhang, and J.Y. Yang, "Kernel ICA: An alternative formulation and its application to face recognition," Pattern Recognition, Vol.38, No.10, Oct. 2005, pp.1784-1787. https://doi.org/10.1016/j.patcog.2005.01.023
  10. J. Moody and C. Darken, "Learning with Localized Receptive Fields," Proc. 1988 Connectionist Models Summer School, Morgan Kaufmann, San Mateo, CA, 1989, pp.133-143.
  11. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning; Data Mining, Inference and Prediction. New York: Springer, 2001.
  12. A.K. Jain, R.P.W. Duin, J. Mao, "Statistical Pattern Recognition: A Review," IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.22, No.1, Jan. 2000, pp.4-37. https://doi.org/10.1109/34.824819
  13. K. M. Tao, "A closer look at the radial basis function (RBF) networks," Proc. 27th Asilomar Conf. on Signals, Systems and Computers, Pacific Grove, CA, Nov. 1993.
  14. Y. Hamamoto, S.Uchimura, S. Tomita, "On the behavior of artificial neural network classifiers in high-dimensional spaces," IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.18, No.5, May 1996, pp.571-574. https://doi.org/10.1109/34.494648
  15. S. Z. Li, and J. Lu, "Face recognition using the nearest feature line method," IEEE Transaction on Neural Networks, Vol.10, No.2, 1999, pp.439-443. https://doi.org/10.1109/72.750575
  16. Y. Pang, Y. Yuan, and X. Li, "Generalized nearest feature line for subspace learning," Electron. Letters, Vol.43, No.20, 2007, pp.1079-1080. https://doi.org/10.1049/el:20072176
  17. J. Lu, Yap-Peng Tan, "Uncorrelated Discriminant Nearest Feature Line Analysis for Face Recognition," IEEE Signal Processing Letters, Vol.17, No.2, Feb. 2010, pp.185-188 https://doi.org/10.1109/LSP.2009.2035017
  18. S.-K. Kim, Y.J. Park, K.-A. Toh, and S. Lee, "SVM-based feature extraction for face recognition," Pattern Recognition, Vol.43, No.8, Aug. 2010, pp.2871-2881 https://doi.org/10.1016/j.patcog.2010.03.008
  19. P.J. Phillips, H. Moon, S. Rizvi, and P. Rauss, "The FERET Evaluation Methodology for Face Recognition Algorithms," IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.22, No.10, Oct. 2000, pp.1090-1104. https://doi.org/10.1109/34.879790
  20. A.R. Martinez and R. Benavente, "The AR face database," Technical Report #24, Computer Vision Center (CVC), Jun. 1998.