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Coupled Marginal Fisher Analysis for Low-Resolution Face Recognition

  • Stephen Siena
  • Vishnu Naresh Boddeti
  • B. V. K. Vijaya Kumar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7584)

Abstract

Many scenarios require that face recognition be performed at conditions that are not optimal. Traditional face recognition algorithms are not best suited for matching images captured at a low-resolution to a set of high-resolution gallery images. To perform matching between images of different resolutions, this work proposes a method of learning two sets of projections, one for high-resolution images and one for low-resolution images, based on local relationships in the data. Subsequent matching is done in a common subspace. Experiments show that our algorithm yields higher recognition rates than other similar methods.

Keywords

Face Recognition Linear Discriminant Analysis Recognition Rate Local Binary Pattern Probe Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Yan, S., Xu, D., Zhang, B., Zhang, H.J., Yang, Q., Lin, S.: Graph embedding and extensions: A general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 40–51 (2007)CrossRefGoogle Scholar
  2. 2.
    Zhou, C., Zhang, Z., Yi, D., Lei, Z., Li, S.Z.: Low-resolution face recognition via Simultaneous Discriminant Analysis. In: International Joint Conference on Biometrics (2011)Google Scholar
  3. 3.
    Li, B., Shan, S., Chen, X.: Low-resolution face recognition via coupled locality preserving mappings. IEEE Signal Processing Letters 17, 20–23 (2010)CrossRefGoogle Scholar
  4. 4.
    Biswas, S., Bowyer, K.W., Flynn, P.J.: Multidimensional scaling for matching low-resolution face images. IEEE Transactions on Pattern Analysis and Machine Intelligence (forthcoming)Google Scholar
  5. 5.
    Sirovich, L., Kirby, M.: Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America A 4, 519–524 (1987)CrossRefGoogle Scholar
  6. 6.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)CrossRefGoogle Scholar
  7. 7.
    Belhumeur, P.N., Hespanha, J.: Eigenfaces vs. fisherfaces: Recognition using specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 711–720 (1997)CrossRefGoogle Scholar
  8. 8.
    Baker, S., Kanade, T.: Hallucinating faces. In: IEEE International Conference on Automatic Face and Gesture Recognition (2000)Google Scholar
  9. 9.
    Zou, W.W.W., Yuen, P.C.: Very low resolution face recognition problem. In: IEEE International Conference on Biometrics: Theory Applications and Systems (2010)Google Scholar
  10. 10.
    Hennings-Yeoman, P.H., Baker, S., Kumar, B.V.: Simultaneous super-resolution and feature extraction for recognition of low-resolution faces. In: IEEE Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
  11. 11.
    Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression (PIE) database. In: IEEE International Conference on Automatic Face and Gesture Recognition (2002)Google Scholar
  12. 12.
    Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-PIE. In: IEEE International Conference on Automatic Face and Gesture Recognition (2008)Google Scholar
  13. 13.
    Tao, Q., Veldhuis, R.: Illumination normalization based on simplified local binary patterns for a face verification system. In: Biometrics Symposium at The Biometrics Consortium Conference (2007)Google Scholar
  14. 14.
    Adjeroh, D.A.: On ratio-based color indexing. IEEE Transactions on Image Processing 10, 36–48 (2001)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Stephen Siena
    • 1
  • Vishnu Naresh Boddeti
    • 1
  • B. V. K. Vijaya Kumar
    • 1
  1. 1.Electrical and Computer EngineeringCarnegie Mellon UniversityPittsburghUSA

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