FACE RECOGNITION USING MARGIN-ENHANCED CLASSIFIER IN GRAPH-BASED SPACE

Ju-Chin Chen, Shang-You Shi, Jenn-Jier James Lien

Abstract

In this paper, we develop a face recognition system with the derived subspace learning method, i.e. classifier-concerning subspace, where not only the discriminant structure of data can be preserved but also the classification ability can be explicitly considered by introducing the Mahalanobis distance metric in the subspace. Most of graph-based subspace learning methods find a subspace with the preservation of certain geometric and discriminant structure of data but not explicitly include the classification information from the classifier. Via the distance metric, which is constrained by k-NN classification rule, the pairwise distance relation can be locally adjusted and thus the projected data in the classifier-concerning subspace are more suitable for k-NN classifier. In addition, an iterative procedure is derived to get rid of the overfitting problem. Experimental results show that the proposed system can yield the promising recognition results under various lighting, pose and expression conditions.

References

  1. Bar-Hillel, A., Hertz, T., Shental, N., and Weinshall, D. (2005). Learning a mahalanobis metric from equivalence constraints. Journal of Machine Learning Research, 6: 937-965.
  2. Belkin, M. and Niyogi, P. (2003). Laplacian eigenmaps for dimensionality reduction and data representation. Journal of Neural Computation, 15(6): 1373-1396.
  3. Belhumeur, P.N., Hespanha, J.P., and Kriegman, D.J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7): 711-720.
  4. Cai, D., He, X., and Zhou, K. (2007). Spectral regression for efficient regularized subspace learning. In International Conference on Computer Vision.
  5. Cai, D., He, X., Zhou, K., Han, J., and Bao, H. (2007). Locality sensitive discriminant analysis. In International Joint Conference on Artificial Intelligence.
  6. Etemad, K. and Chellapa, R. (1997). Discriminant analysis for recognition of human face images. Journal of the Optical Society of America A, 14(8): 1724-1733.
  7. Goldberger, J., Roweis, S. T., Hinton, G.E., and Salakhutdinov, R. (2004). Neighbourhood components analysis. In Advances in Neural Information Processing Systems.
  8. He, X. and Niyogi, P. (2003). Locality preserving projections. In Advances in Neural Information Processing Systems.
  9. Murase, H. and Nayar, S. K. (1995). Visual learning and recognition of 3-d objects from appearance. International Journal of Computer Vision, 14(1): 5-24.
  10. Roweis, S. T. and Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500): 2323-2326.
  11. Tenebaum, J. B., de Silva, V. and Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500): 2319- 2323.
  12. Turk, M. and Pentland, A. (1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1): 71-86.
  13. Weinberger, K. Q. and Saul, L. K. (2009). Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research, 10: 209-244.
  14. Xing, E., Ng, A., Jordan, and M. Russell, S. (2003). Distance metric learning, with application to cluster with side information. In Advances in Neural Information Processing Systems.
  15. Yan, S., Xu, D., Zhang, B., Zhang, H.-J., Yang, Q., and Lin, S. (2007). Graph embedding and extension: A general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis Machine Intelligence, 29(1): 40-51.
  16. Ye, J. P. and Wang, T. (2006). Regularized discriminant analysis for high dimensional, low sample size data. In International Conference. on Knowledge Discovery and Data Mining.
  17. Zheng, Z., Yang, F., Tan, W., Jia, J. and Yang, J. (2007). Gabor feature-based face recognition using supervised locality preserving projection. Signal Processing, 87: 2473-2483.
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Paper Citation


in Harvard Style

Chen J., Shi S. and James Lien J. (2010). FACE RECOGNITION USING MARGIN-ENHANCED CLASSIFIER IN GRAPH-BASED SPACE . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-029-0, pages 382-388. DOI: 10.5220/0002831903820388


in Bibtex Style

@conference{visapp10,
author={Ju-Chin Chen and Shang-You Shi and Jenn-Jier James Lien},
title={FACE RECOGNITION USING MARGIN-ENHANCED CLASSIFIER IN GRAPH-BASED SPACE},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={382-388},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002831903820388},
isbn={978-989-674-029-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)
TI - FACE RECOGNITION USING MARGIN-ENHANCED CLASSIFIER IN GRAPH-BASED SPACE
SN - 978-989-674-029-0
AU - Chen J.
AU - Shi S.
AU - James Lien J.
PY - 2010
SP - 382
EP - 388
DO - 10.5220/0002831903820388