Image Preprocessing for Illumination Invariant Face Verification

Authors

DOI:

https://doi.org/10.26636/jtit.2010.4.1092

Keywords:

DLDA, face verification, histogram normalization, homomorphic filtering, illumination normalization, LDA, PCA, preprocessing techniques, quotient image, retinex

Abstract

Performance of the face verification system depend on many conditions. One of the most problematic is varying illumination condition. In this paper 14 normalization algorithms based on histogram normalization, illumination properties and the human perception theory were compared using 3 verification methods. The results obtained from the experiments showed that the illumination preprocessing methods significantly improves the verification rate and it’s a very important step in face verification system.

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References

[1] N. Kanwisher, J. McDermott, and M. Chun, „The fusiform face areas: a module in human extrastriate cortex specialized for face perception”, J. Neurosci., vol. 17, no. 11, pp. 4302-4311, 1997.
View in Google Scholar

[2] Y. Adini, Y. Moses, and S. Ullman, „Face recognition: the problem of compensating for illumination changes”, IEEE Trans. Patt. Anal. Mach. Intell., vol. 19, pp. 721-732, 1997.
View in Google Scholar

[3] P. J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss, „The FERET evaluation methodology for face-recognition algorithms”, Trans. PAMI, vol. 22, pp. 1090-1104, 2000.
View in Google Scholar

[4] P. J. Phillips et al., „Face recognition vendor test 2002: evaluation report”, Tech. Rep. NISTIR 6965, National Institute of Standards and Technology, 2003.
View in Google Scholar

[5] P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer and W. Worek, „Preliminary Face Recognition Grand Challenge Results”, in Proc. 7th Int. Conf. Autom. Face Gesture Recogn., Southampton, UK, 2006, pp. 15-24.
View in Google Scholar

[6] P. J. Phillips et al., „FRVT 2006 and ICE 2006 large-scale results”, NISTIR 7408, National Institute of Standards and Technology, 2007.
View in Google Scholar

[7] A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, „From few to many: illumination cone models for face recognition under variable lighting and pose”, IEEE Trans. Patt. Anal. Mach. Intell., vol. 23, no. 6, pp. 643-660, 2001.
View in Google Scholar

[8] M. Vasilescu and D. Terzopoulos, „Multilinear analysis of image ensembles: tensor-faces”, in Proc. Eur. Conf. Comp. Vision ECCV 2002, Copenhagen, Denmark, 2002, pp. 447-460.
View in Google Scholar

[9] M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions. New York: Dover, 1964.
View in Google Scholar

[10] M. Evans, N. Hastings, and B. Peacock, Statistical Distributions. 2nd ed. New York: Wiley, 1993.
View in Google Scholar

[11] E. Land and J. McCann, „Lightness and retinex theory”, J. Opt. Soc. Amer., vol. 61, pp. 1-11, 1971.
View in Google Scholar

[12] D. J. Jobson, Z. Rahman, and G. A. Woodell, „Properties and performance of a center/surround retinex”, IEEE Trans. Image Process, vol. 6, no. 3, pp. 451-462, 1997.
View in Google Scholar

[13] Z. Rahman, G. Woodell, and D. Jobson, „A comparison of the multi-scale retinex with other image enhancement techniques”, in Proc. 50th IS&T Anniv. Conf., Boston, USA, 1997.
View in Google Scholar

[14] Y. K. Park, S. L. Park, and J. K. Kim, „Retinex method based on adaptive smoothing for illumination invariant face recognition”, Sig. Proces., vol. 88, no. 8, pp. 1929-1945, 2008.
View in Google Scholar

[15] R. Gonzalez and R. Woods, Digital Image Processing. 2nd ed. Boston: Addison-Wesley Longman, 1992.
View in Google Scholar

[16] H. Wang, S. Z. Li, and Y. Wang, „Face recognition under varying lighting condition using self quotient image”, in Proc. 6th IEEE Int. Conf. Autom. Face Gesture Recog., Seoul, Korea, 2004, pp. 819-824.
View in Google Scholar

[17] V. Štruc and N. Pavešić, „Performance evaluation of photometric normalization techniques for illumination invariant face recognition”, in Advances in Face Image Analysis: Techniques and Technologies, Y. J. Zhang, Ed. Hershey: IGI Global, 2010.
View in Google Scholar

[18] K. Fukunaga, Introduction to Statistical Pattern Recognition. San Diego: Academic Press, 1992.
View in Google Scholar

[19] W. Skarbek, K. Kucharski, and M. Bober, „Dual LDA for face recognition”, Fundamenta Informaticae, vol. 61, pp. 303-334, 2004.
View in Google Scholar

[20] K. Pearson, „On lines and planes of closest fit to systems of points in space”, Philosophical Mag., vol. 2, no. 6, pp. 559-572, 1901.
View in Google Scholar

[21] M. A. Turk and A. P. Pentland, „Face recognition using eigenfaces”, in Proc. IEEE Comp. Soc. Conf. Comp. Vision Patt. Recogn. CVPR’91, Maui, USA, 1991, pp. 586-591.
View in Google Scholar

[22] „Information technology - Multimedia content description interface - Part 3: Visual”, ISO/IEC 15938-3:2002, 2002.
View in Google Scholar

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Submitted

2023-06-27

Published

2010-12-30

Issue

Section

RESEARCH ARTICLES

How to Cite

[1]
M. Leszczyński, “ Image Preprocessing for Illumination Invariant Face Verification”, JTIT, vol. 42, no. 4, pp. 19–25, Dec. 2010, doi: 10.26636/jtit.2010.4.1092.