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Class Dependent 2D Correlation Filter for Illumination Tolerant Face Recognition

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7143))

Abstract

This paper proposes a class dependent 2D correlation filtering technique in frequency domain for illumination tolerant face recognition. The technique is based on the frequency domain correlation between phase spectrum of reconstructed image and the phase spectrum of optimum correlation filter. The optimization is achieved by minimizing the energy at the correlation plane due to resonstructed image and maximizing the corelation peak. The synthesis of optimum filter is developed by using the projecting image. Peak to side lobe ratio (PSR) is taken as the metric for recogntion and classification. The performance evaluation of this technique is validated by comparing performance of other unconstrained filtering techniques using benchmark databases (Yale B and PIE) and better results are obtained.

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References

  1. Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression database. IEEE Transactions on Pattern Analysis and Mlachine Intelligence 25(12), 1615–1618 (2003)

    Article  Google Scholar 

  2. Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intelligence 23(6), 643–660 (2001)

    Article  Google Scholar 

  3. Adini, Y., Moses, Y., Ullman, S.: Face recognition: The problem of compensating for changes in illumination direction. IEEE Transaction of Pattern Analysis and Machine Intelligence 19(7) (1997)

    Google Scholar 

  4. Mahalanobis, A., Vijaya Kumar, B.V.K., Song, S., Sims, S., Epperson, J.: Unconstrained correlation filter. App. Opt. 33, 3751–3759 (1994)

    Article  Google Scholar 

  5. Vijaya Kumar, B.V.K., Mahalanobis, A., Juday, R.: Correlation Pattern Recognition. Cambridge University Press (2005)

    Google Scholar 

  6. Vijaya Kumar, B.V.K., Savvides, M., Xie, C.: Correlation pattern recognition for face recognition. Proc. IEEE 94 (2006)

    Google Scholar 

  7. Savvides, M., Vijaya Kumar, B.V.K.: Illumination Normalization using Logarithm Transforms for Face Authentication. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 549–556. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  8. Savvides, M., Heo, J., Abiantun, R., Xie, C., Vijaya Kumar, B.V.K.: Partial and holistic face recognition on frgc-ii data using supportvector machines. In: Proc. of IEEE ICCVPR, vol. 48 (2006)

    Google Scholar 

  9. Savvides, M., Heo, J., Abiantun, R., Xie, C., Vijaya Kumar, B.V.K.: Class dependent kernel discrete cosine transform features for enhanced holistic face recognition in frgc-ii. In: Proc. of IEEE Int. Conf. on Acoustics, Speech and Signal Processing, vol. II, p. 185 (2006)

    Google Scholar 

  10. Belhumeur, P.N., Kreigman, D.J.: Eigenfaces vs fisherfaces: recognition using class specific linear projection. IEEE Tran. Pattern Analysis and Machine Intelligence 20(7) (1997)

    Google Scholar 

  11. Wang, S., Draper, J., Ross, J.: Analyzing pca-based face recognition algorithms: Eigenvector selection and distance measures. ABCD (July 2001)

    Google Scholar 

  12. Sim, T., Kanade, T.: Combining models and exemplars for face recognition: An illumination example. In: Proceedings of the CVPR (December 2001)

    Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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Banerjee, P.K., Chandra, J.K., Datta, A.K. (2012). Class Dependent 2D Correlation Filter for Illumination Tolerant Face Recognition. In: Kundu, M.K., Mitra, S., Mazumdar, D., Pal, S.K. (eds) Perception and Machine Intelligence. PerMIn 2012. Lecture Notes in Computer Science, vol 7143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27387-2_42

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  • DOI: https://doi.org/10.1007/978-3-642-27387-2_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27386-5

  • Online ISBN: 978-3-642-27387-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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