Blur and Illumination Robust Face Recognition via Set-Theoretic Characterization

We address the problem of unconstrained face recognition from remotely acquired images. The main factors that make this problem challenging are image degradation due to blur, and appearance variations due to illumination and pose. In this paper, we address the problems of blur and illumination. We show that the set of all images obtained by blurring a given image forms a convex set. Based on this set-theoretic characterization, we propose a blur-robust algorithm whose main step involves solving simple convex optimization problems. We do not assume any parametric form for the blur kernels, however, if this information is available it can be easily incorporated into our algorithm. Furthermore, using the low-dimensional model for illumination variations, we show that the set of all images obtained from a face image by blurring it and by changing the illumination conditions forms a bi-convex set. Based on this characterization, we propose a blur and illumination-robust algorithm. Our experiments on a challenging real dataset obtained in uncontrolled settings illustrate the importance of jointly modeling blur and illumination.

[1]  Pat Hanrahan,et al.  A signal-processing framework for reflection , 2004, ACM Trans. Graph..

[2]  Gerard de Haan,et al.  Low Cost Robust Blur Estimator , 2006, 2006 International Conference on Image Processing.

[3]  Timo Ahonen,et al.  Recognition of blurred faces using Local Phase Quantization , 2008, 2008 19th International Conference on Pattern Recognition.

[4]  David W. Jacobs,et al.  In search of illumination invariants , 2001, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[5]  Gerard de Haan,et al.  Adaptive Image Restoration Based on Local Robust Blur Estimation , 2007, ACIVS.

[6]  Marios Savvides,et al.  Correlation Pattern Recognition for Face Recognition , 2006, Proceedings of the IEEE.

[7]  B. V. K. Vijaya Kumar,et al.  Eigenphases vs eigenfaces , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[8]  Frédo Durand,et al.  Understanding Blind Deconvolution Algorithms , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[10]  David W. Jacobs,et al.  Surface Dependent Representations for Illumination Insensitive Image Comparison , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  William H. Richardson,et al.  Bayesian-Based Iterative Method of Image Restoration , 1972 .

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

[13]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Anton van den Hengel,et al.  Semidefinite Programming , 2014, Computer Vision, A Reference Guide.

[16]  Masashi Nishiyama,et al.  Facial Deblur Inference Using Subspace Analysis for Recognition of Blurred Faces , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Nathan Intrator,et al.  Blurred face recognition via a hybrid network architecture , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[18]  Rama Chellappa,et al.  Evaluation of state-of-the-art algorithms for remote face recognition , 2010, 2010 IEEE International Conference on Image Processing.

[19]  P. L. Combettes,et al.  The Convex Feasibility Problem in Image Recovery , 1996 .

[20]  Patrick L. Combettes,et al.  Image restoration subject to a total variation constraint , 2004, IEEE Transactions on Image Processing.

[21]  H. Trussell,et al.  The feasible solution in signal restoration , 1984 .

[22]  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).

[23]  Frédo Durand,et al.  Understanding and evaluating blind deconvolution algorithms , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Josef Kittler,et al.  Robust albedo estimation from face image under unknown illumination , 2008, SPIE Defense + Commercial Sensing.

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

[26]  Deepa Kundur,et al.  Blind Image Deconvolution , 2001 .

[27]  Russell A. Epstein,et al.  5/spl plusmn/2 eigenimages suffice: an empirical investigation of low-dimensional lighting models , 1995, Proceedings of the Workshop on Physics-Based Modeling in Computer Vision.

[28]  Pawan Sinha,et al.  Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About , 2006, Proceedings of the IEEE.

[29]  Anat Levin,et al.  Blind Motion Deblurring Using Image Statistics , 2006, NIPS.

[30]  David J. Kriegman,et al.  From few to many: generative models for recognition under variable pose and illumination , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

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

[32]  Deepa Kundur,et al.  Blind image deconvolution revisited , 1996 .

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

[34]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression Database , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  Thomas S. Huang,et al.  Close the loop: Joint blind image restoration and recognition with sparse representation prior , 2011, 2011 International Conference on Computer Vision.

[36]  Rama Chellappa,et al.  A Blur-Robust Descriptor with Applications to Face Recognition , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Rama Chellappa,et al.  Remote identification of faces: Problems, prospects, and progress , 2012, Pattern Recognit. Lett..

[38]  David J. Kriegman,et al.  Acquiring linear subspaces for face recognition under variable lighting , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  Ville Ojansivu,et al.  Blur Insensitive Texture Classification Using Local Phase Quantization , 2008, ICISP.

[40]  J. Biemond,et al.  Basic Methods for Image Restoration and Identification , 2009 .

[41]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[42]  Edwin R. Hancock,et al.  Estimating the albedo map of a face from a single image , 2005, IEEE International Conference on Image Processing 2005.

[43]  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..

[44]  Alan C. Bovik,et al.  The Essential Guide to Image Processing , 2009, J. Electronic Imaging.

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

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

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

[48]  Rama Chellappa,et al.  Robust Estimation of Albedo for Illumination-invariant Matching and Shape Recovery , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[49]  Sundaresh Ram,et al.  Removing Camera Shake from a Single Photograph , 2009 .