Total variation models for variable lighting face recognition

In this paper, we present the logarithmic total variation (LTV) model for face recognition under varying illumination, including natural lighting conditions, where we rarely know the strength, direction, or number of light sources. The proposed LTV model has the ability to factorize a single face image and obtain the illumination invariant facial structure, which is then used for face recognition. Our model is inspired by the SQI model but has better edge-preserving ability and simpler parameter selection. The merit of this model is that neither does it require any lighting assumption nor does it need any training. The LTV model reaches very high recognition rates in the tests using both Yale and CMU PIE face databases as well as a face database containing 765 subjects under outdoor lighting conditions

[1]  B. V. K. Vijaya Kumar,et al.  "Corefaces" - robust shift invariant PCA based correlation filter for illumination tolerant face recognition , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[2]  Tony F. Chan,et al.  Aspects of Total Variation Regularized L[sup 1] Function Approximation , 2005, SIAM J. Appl. Math..

[3]  Josef Kittler,et al.  A comparison of photometric normalisation algorithms for face verification , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[4]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[5]  Penio S. Penev,et al.  Local feature analysis: A general statistical theory for object representation , 1996 .

[6]  Timothy F. Cootes,et al.  Automatic Interpretation and Coding of Face Images Using Flexible Models , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  T. Chan,et al.  Edge-preserving and scale-dependent properties of total variation regularization , 2003 .

[9]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[10]  Pradeep K. Khosla,et al.  "Corefaces" - robust shift invariant PCA based correlation filter for illumination tolerant face recognition , 2004, CVPR 2004.

[11]  Lei Zhang,et al.  Face recognition under variable lighting using harmonic image exemplars , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[12]  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..

[13]  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..

[14]  Alex Pentland,et al.  Bayesian face recognition , 2000, Pattern Recognit..

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

[16]  Jr. Thomas G. Stockham,et al.  Image processing in the context of a visual model , 1972 .

[17]  Stefano Alliney,et al.  Digital filters as absolute norm regularizers , 1992, IEEE Trans. Signal Process..

[18]  Y. J. Tejwani,et al.  Robot vision , 1989, IEEE International Symposium on Circuits and Systems,.

[19]  Rama Chellappa,et al.  Robust image based face recognition , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[20]  Pat Hanrahan,et al.  A signal-processing framework for inverse rendering , 2001, SIGGRAPH.

[21]  Guodong Guo,et al.  Support vector machines for face recognition , 2001, Image Vis. Comput..

[22]  Wen Gao,et al.  Illumination normalization for robust face recognition against varying lighting conditions , 2003, 2003 IEEE International SOI Conference. Proceedings (Cat. No.03CH37443).

[23]  David J. Kriegman,et al.  What is the set of images of an object under all possible lighting conditions? , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[24]  Wotao Yin,et al.  Image Cartoon-Texture Decomposition and Feature Selection Using the Total Variation Regularized L1 Functional , 2005, VLSM.

[25]  P. Hanrahan,et al.  On the relationship between radiance and irradiance: determining the illumination from images of a convex Lambertian object. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[26]  Zia-ur Rahman,et al.  A multiscale retinex for bridging the gap between color images and the human observation of scenes , 1997, IEEE Trans. Image Process..

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

[28]  Michael Elad,et al.  A Variational Framework for Retinex , 2002, IS&T/SPIE Electronic Imaging.

[29]  Donald Goldfarb,et al.  Second-order cone programming , 2003, Math. Program..

[30]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[31]  Vladimir Brajovic,et al.  Model for reflectance perception in vision , 2003, SPIE Microtechnologies.

[32]  Rama Chellappa,et al.  Robust Face Recognition Using Symmetric Shape-from-Shading , 1999 .

[33]  Guodong Guo,et al.  Face recognition by support vector machines , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

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

[35]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[36]  Dorin Comaniciu,et al.  Illumination normalization for face recognition and uneven background correction using total variation based image models , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[37]  S. Osher,et al.  G-Norm Properties of Bounded Variation Regularization , 2004 .

[38]  Rama Chellappa,et al.  Illumination-insensitive face recognition using symmetric shape-from-shading , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

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

[40]  Vladimir Brajovic,et al.  Brightness perception, dynamic range and noise: a unifying model for adaptive image sensors , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[41]  Michael Elad,et al.  Reduced complexity Retinex algorithm via the variational approach , 2003, J. Vis. Commun. Image Represent..

[42]  Greg Turk,et al.  LCIS: a boundary hierarchy for detail-preserving contrast reduction , 1999, SIGGRAPH.

[43]  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.

[44]  S. Osher,et al.  Slope and $G$-set characterization of set-valued functions and applications to non-differentiable optimization problems , 2005 .

[45]  Norbert Krüger,et al.  Face recognition by elastic bunch graph matching , 1997, Proceedings of International Conference on Image Processing.

[46]  Ronen Basri,et al.  Lambertian reflectance and linear subspaces , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[47]  K. Hohn,et al.  Determining Lightness from an Image , 2004 .

[48]  Ralph Gross,et al.  An Image Preprocessing Algorithm for Illumination Invariant Face Recognition , 2003, AVBPA.

[49]  Alexei A. Efros,et al.  Fast bilateral filtering for the display of high-dynamic-range images , 2002 .

[50]  Wotao Yin,et al.  Second-order Cone Programming Methods for Total Variation-Based Image Restoration , 2005, SIAM J. Sci. Comput..

[51]  Haitao Wang,et al.  Generalized quotient image , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[52]  Mila Nikolova,et al.  Minimizers of Cost-Functions Involving Nonsmooth Data-Fidelity Terms. Application to the Processing of Outliers , 2002, SIAM J. Numer. Anal..

[53]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..