An Illumination Invariant Face Recognition Scheme to Combining Normalized Structural Descriptor with Single Scale Retinex

Illumination variation is still a challenging issue to address in face recognition. Retinex scheme is effective to face images under small illumination variation, but its performance drop when illumination variation is large. We further analyze the normalized images under large illumination variation and we find that the illumination variation has not been removed thoroughly in these images. Structural similarity is one of image similarity metrics similar to human perception. From SSIM, we extract the structure related component and name it as Normalized Structure Descriptor. It is clear that NSD is robust to illumination variation. We propose a scheme to combining Normalized Structural Descriptor with Single Scale Retinex. In our scheme NSD is extracted from the normalized image from SSR. And the face recognition is performed by the similarity of NSD. The experimental results on the Yale Face Database B and Extended Yale Face Database B show that our approach has performance comparable to state-of-the-art approaches.

[1]  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..

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

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

[4]  Wei-Yun Yau,et al.  Relative gradients for image lighting correction , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[5]  Meng Joo Er,et al.  A Novel Local Illumination Normalization Approach for Face Recognition , 2011, ISNN.

[6]  Biao Wang,et al.  Illumination Normalization Based on Weber's Law With Application to Face Recognition , 2011, IEEE Signal Processing Letters.

[7]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[8]  Ronen Basri,et al.  Lambertian Reflectance and Linear Subspaces , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Yuan Yan Tang,et al.  Face Recognition Under Varying Illumination Using Gradientfaces , 2009, IEEE Transactions on Image Processing.

[10]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[12]  Alice J. O'Toole,et al.  FRVT 2006 and ICE 2006 large-scale results , 2007 .

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

[14]  Dorin Comaniciu,et al.  Total variation models for variable lighting face recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  David J. Kriegman,et al.  Nine points of light: acquiring subspaces for face recognition under variable lighting , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[16]  John D. Austin,et al.  Adaptive histogram equalization and its variations , 1987 .

[17]  Meng Joo Er,et al.  Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[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..