An Improved Illumination Normalization based on Anisotropic Smoothing for Face Recognition
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.
[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.
[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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:53085", author = "Sanghoon Kim and Sun-Tae Chung and Souhwan Jung and Seongwon Cho", title = "An Improved Illumination Normalization based on Anisotropic Smoothing for Face Recognition", 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.", volume = "2", number = "1", pages = "55-7", }