A Study on the Effective Approach to Illumination-Invariant Face Recognition Based on a Single Image

In this paper, the methods for single image-based face recognition under varying lighting are reviewed. Meanwhile, some representative methods as well as their combinations are evaluated by experiments, and the underlying principle of the experimental results is investigated. According to our investigation, it is almost impossible to attain a satisfied face recognition result by using only one facial descriptor/representation especially under drastically varying illuminations. However, the "two-step" framework, including an illumination preprocessing and an illumination-insensitive facial features extraction, could be an effective approach to addressing this problem. We further study what are the appropriate illumination preprocessing and feature extraction for this framework.

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

[2]  Amnon Shashua,et al.  The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  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).

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

[5]  Dao-Qing Dai,et al.  Face Recognition Using Dual-Tree Complex Wavelet Features , 2009, IEEE Transactions on Image Processing.

[6]  Kin-Man Lam,et al.  Face recognition under varying illumination based on a 2D face shape model , 2005, Pattern Recognit..

[7]  Majid Ahmadi,et al.  Illumination invariant feature extraction and mutual-information-based local matching for face recognition under illumination variation and occlusion , 2011, Pattern Recognit..

[8]  Jian-Jun Zhang,et al.  Self quotient image for face recognition , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

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

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

[11]  Wen Gao,et al.  Face recognition under generic illumination based on harmonic relighting , 2005, Int. J. Pattern Recognit. Artif. Intell..

[12]  Raghu Machiraju,et al.  A bilinear illumination model for robust face recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

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

[14]  Jiebo Luo,et al.  A Subspace Model-Based Approach to Face Relighting Under Unknown Lighting and Poses , 2008, IEEE Transactions on Image Processing.

[15]  Xuan Zou,et al.  Illumination Invariant Face Recognition: A Survey , 2007, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems.

[16]  Lei Zhang,et al.  Face synthesis and recognition from a single image under arbitrary unknown lighting using a spherical harmonic basis morphable model , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[17]  Rama Chellappa,et al.  Symmetric Shape-from-Shading Using Self-ratio Image , 2001, International Journal of Computer Vision.

[18]  Chong-Ho Choi,et al.  Shadow compensation in 2D images for face recognition , 2007, Pattern Recognit..

[19]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[20]  Jian-Huang Lai,et al.  Extraction of illumination invariant facial features from a single image using nonsubsampled contourlet transform , 2010, Pattern Recognit..

[21]  Chu-Song Chen,et al.  Illumination invariant feature extraction based on natural images statistics — Taking face images as an example , 2011, CVPR 2011.

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

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

[24]  Jian-Huang Lai,et al.  Face recognition using holistic Fourier invariant features , 2001, Pattern Recognit..

[25]  Yuan Yan Tang,et al.  Multiscale facial structure representation for face recognition under varying illumination , 2009, Pattern Recognit..

[26]  Martin D. Levine,et al.  Face Recognition Using the Discrete Cosine Transform , 2001, International Journal of Computer Vision.

[27]  Alain Trouvé,et al.  A deformation and lighting insensitive metric for face recognition based on dense correspondences , 2011, CVPR 2011.

[28]  Christophe Garcia,et al.  A Wavelet-based Framework for Face Recognition , 1998 .

[29]  Thomas Vetter,et al.  Face Recognition Based on Fitting a 3D Morphable Model , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

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

[31]  Hartmut Neven,et al.  The Bochum / USC Face Recognition Systemand How it Fared in the FERET Phase , 1998 .

[32]  Shimon Ullman,et al.  Face Recognition: The Problem of Compensating for Changes in Illumination Direction , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

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

[34]  David J. Kriegman,et al.  Pose, illumination and expression invariant pairwise face-similarity measure via Doppelgänger list comparison , 2011, 2011 International Conference on Computer Vision.

[35]  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..

[36]  Patrick J. Flynn,et al.  Overview of the face recognition grand challenge , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).