Visual illumination compensation for face images using light mapping matrix

Illumination variation is a challenging issue in face recognition. In many conventional approaches the low-frequency coefficients are usually discarded in order to compensate the illumination variations, and hence degrade the visual quality. To deal with these problems, an adaptive normalisation-based method is proposed in this study. Each image is normalised according to its lighting attribute by mapping the low-frequency components to the normal condition instead of discarding them by applying a novel statistical concept called light mapping matrix. The method preserves the low-frequency facial features, maximising the intra-individual correlation and improves the visual quality of face images in different lighting conditions.

[1]  V. P. Vishwakarma,et al.  A Novel Approach for Face Recognition Using DCT Coefficients Re-scaling for Illumination Normalization , 2007, 15th International Conference on Advanced Computing and Communications (ADCOM 2007).

[2]  Bo Gu,et al.  Single image visibility enhancement in gradient domain , 2012 .

[3]  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..

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

[5]  K. Faez,et al.  Use of matrix polar decomposition for illumination-tolerant face recognition in discrete cosine transform domain , 2011 .

[6]  Lian Zhichao,et al.  An efficient illumination normalization method in a transformed domain , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

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

[8]  Nicolae Vizireanu,et al.  Quantisation-based video watermarking in the wavelet domain with spatial and temporal redundancy , 2011 .

[9]  Jian-Huang Lai,et al.  Normalization of Face Illumination Based on Large-and Small-Scale Features , 2011, IEEE Transactions on Image Processing.

[10]  Sudipta Mukhopadhyay,et al.  Single image fog removal using anisotropic diffusion , 2012 .

[11]  Shen-Chuan Tai,et al.  Level-base compounded logarithmic curve function for colour image enhancement , 2012 .

[12]  Ali Aghagolzadeh,et al.  Feature extraction using discrete cosine transform and discrimination power analysis with a face recognition technology , 2010, Pattern Recognit..

[13]  Virendra P. Vishwakarma,et al.  Adaptive Histogram Equalization and Logarithm Transform with Rescaled Low Frequency DCT Coefficients for Illumination Normalization , 2009 .

[14]  Claudio A. Perez,et al.  Genetic improvements in illumination compensation by the discrete cosine transform and local normalization for face recognition , 2008, International Symposium on Optomechatronic Technologies.

[15]  Wen Gao,et al.  Lighting Aware Preprocessing for Face Recognition across Varying Illumination , 2010, ECCV.

[16]  B. N. Chatterji,et al.  Contrast enhancement of dark images using stochastic resonance , 2012 .

[17]  Chang Wook Ahn,et al.  Image matching using peak signal-to-noise ratio-based occlusion detection , 2012 .

[18]  Jian-Huang Lai,et al.  Face illumination normalization on large and small scale features , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  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).

[20]  Reshma Khemchandani,et al.  Twin Support Vector Machines for Pattern Classification , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[22]  Madan Gopal,et al.  Least squares twin support vector machines for pattern classification , 2009, Expert Syst. Appl..

[23]  Rabab Kreidieh Ward,et al.  Adaptive Region-Based Image Enhancement Method for Robust Face Recognition Under Variable Illumination Conditions , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

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

[25]  Radu Mihnea Udrea,et al.  Iterative generalization of morphological skeleton , 2007, J. Electronic Imaging.