An Improved Illumination Normalization based on Anisotropic Smoothing for Face Recognition

Authors: Sanghoon Kim, Sun-Tae Chung, Souhwan Jung, Seongwon Cho

Abstract:

Robust face recognition under various illumination environments is very difficult and needs to be accomplished for successful commercialization. In this paper, we propose an improved illumination normalization method for face recognition. Illumination normalization algorithm based on anisotropic smoothing is well known to be effective among illumination normalization methods but deteriorates the intensity contrast of the original image, and incurs less sharp edges. The proposed method in this paper improves the previous anisotropic smoothing-based illumination normalization method so that it increases the intensity contrast and enhances the edges while diminishing the effect of illumination variations. Due to the result of these improvements, face images preprocessed by the proposed illumination normalization method becomes to have more distinctive feature vectors (Gabor feature vectors) for face recognition. Through experiments of face recognition based on Gabor feature vector similarity, the effectiveness of the proposed illumination normalization method is verified.

Keywords: Illumination Normalization, Face Recognition, Anisotropic smoothing, Gabor feature vector.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1331299

References:

[1] S. Z. Li and A. K. Jain, Handbook of Face Recognition, Springer, 2004.
[2] Y. Adini, Y. Moses, and S. Ullman, "Face Recognition: The problem of compensating for changes in illumination direction," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.19, No.7, pp.721-732, July 1997.
[3] J. Short, J. Kittler and K. Messer, "A Comparison of Photometric Normalization Algorithm for Face Verification," Proc. Of 6th IEEE Int-l Conf. on Automatic Face and Gesture Recognition (FGR-04), pp.254- 259, May 2004.
[4] R. Gross and V. Brajovic, "An image preprocessing algorithm for illumination invariant face recognition," In Audio-and Video-Based Biometric Person Authentication, Vol.2688, pp.10-18, June 2003.
[5] L. Wiskott, J. M. Fellous, N. Kuiger, and C. von der Malsburg, "Face Recognition by Elastic Bunch Graph Matching," Pattern Analysis and Machine Intelligence, IEEE Transactions, Vol.19, pp.775-779, July 1997.
[6] P. J. Phillips, P. Grother, R. J Micheals, D. M. Blackburn, E. Tabassi, and J.M. Bone. FRVT 2002: Overview and Summary, March 2003.
[7] B. Horn, Robot Vision, MIT Press, 1986.
[8] W. Press, S. Teukolsky, W. Vetterling, B. Flannery, Numerical Recipes in C, Cambridge University Press, 1992.
[9] CMU PIE face database, http://www.ri.cmu/edu/projects/project_418 .html
[10] A. Georghiades, P. Belhumeur and D. Kriegman, "From few to many: Illumination cone models for face recognition under variable lighting and pose," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No.6, pp. 643-660, 2001.