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Green Image
Issue No. 06 - June (2012 vol. 34)
ISSN: 0162-8828
pp: 1220-1226
S. Taheri , Dept. of Comput. Sci., Univ. of Maryland, College Park, MD, USA
R. Gopalan , Video & Multimedia Dept., AT&T Labs. - Res., Middletown, NJ, USA
P. Turaga , Schools of Arts, Media, Eng., & Electr. Eng., Arizona State Univ., Tempe, AZ, USA
R. Chellappa , Dept. of Electr. & Comput. Eng., Univ. of Maryland, College Park, MD, USA
Understanding the effect of blur is an important problem in unconstrained visual analysis. We address this problem in the context of image-based recognition by a fusion of image-formation models and differential geometric tools. First, we discuss the space spanned by blurred versions of an image and then, under certain assumptions, provide a differential geometric analysis of that space. More specifically, we create a subspace resulting from convolution of an image with a complete set of orthonormal basis functions of a prespecified maximum size (that can represent an arbitrary blur kernel within that size), and show that the corresponding subspaces created from a clean image and its blurred versions are equal under the ideal case of zero noise and some assumptions on the properties of blur kernels. We then study the practical utility of this subspace representation for the problem of direct recognition of blurred faces by viewing the subspaces as points on the Grassmann manifold and present methods to perform recognition for cases where the blur is both homogenous and spatially varying. We empirically analyze the effect of noise, as well as the presence of other facial variations between the gallery and probe images, and provide comparisons with existing approaches on standard data sets.
image restoration, convolution, differential geometry, face recognition, probe images, blur-robust descriptor, face recognition, blur effect, visual analysis, image-based recognition, image-formation models, differential geometric tools, image blurring, image convolution, orthonormal basis functions, zero noise, blur kernel property, subspace representation, Grassmann manifold, facial variations, gallery images, Kernel, Noise, Face, Face recognition, Manifolds, Probes, Convolution, face recognition., Blur, convolution, subspace, Grassmann manifold

S. Taheri, R. Gopalan, P. Turaga and R. Chellappa, "A Blur-Robust Descriptor with Applications to Face Recognition," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 1220-1226, 2012.
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