Image Preprocessing for Illumination Invariant Face Verification

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

[1]  Wladyslaw Skarbek,et al.  Dual LDA for Face Recognition , 2004, Fundam. Informaticae.

[2]  Vitomir Struc,et al.  Photometric Normalization Techniques for Illumination Invariance , 2011 .

[3]  Rahman Zia-ur,et al.  A Comparison of the Multiscale Retinex With Other Image Enhancement Techniques , 1997 .

[4]  Patrick J. Flynn,et al.  Preliminary Face Recognition Grand Challenge Results , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[5]  Zia-ur Rahman,et al.  Properties and performance of a center/surround retinex , 1997, IEEE Trans. Image Process..

[6]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Joongkyu Kim,et al.  Retinex method based on adaptive smoothing for illumination invariant face recognition , 2008, Signal Process..

[8]  Demetri Terzopoulos,et al.  Multilinear Analysis of Image Ensembles: TensorFaces , 2002, ECCV.

[9]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[11]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  M. Evans,et al.  Statistical Distributions, Third Edition , 2001 .

[13]  Irene A. Stegun,et al.  Handbook of Mathematical Functions. , 1966 .

[14]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[15]  W. Marsden.  I and J , 2012 .

[16]  N. Kanwisher,et al.  The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception , 1997, The Journal of Neuroscience.

[17]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Haitao Wang,et al.  Face recognition under varying lighting conditions using self quotient image , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[19]  Shimon Ullman,et al.  Face Recognition: The Problem of Compensating for Changes in Illumination Direction , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  E. Land,et al.  Lightness and retinex theory. , 1971, Journal of the Optical Society of America.

[21]  P. Phillips,et al.  1 FACE RECOGNITION VENDOR TEST 2002 : EVALUATION REPORT , 2003 .