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Tags: Recognition, Face, Applications, with, Descriptor, BlurRobust, A BlurRobust Descriptor with Applications to Face Recognition,
 
 
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A Blur-Robust Descriptor with Applications to Face Recognition
Post: #1

A Blur-Robust Descriptor with Applications
to Face Recognition:
Abstract—Understanding the effect of blur is an important problem in
unconstrained visual analysis. We address this problem in the context of imagebased
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.
Index Terms—Blur, convolution, subspace, Grassmann manifold, face
Post: #2
Understanding the effect of blurring is a major problem in unrestricted visual analysis. We approach this problem in the context of image-based recognition through a fusion of models of image formation and differential geometric tools. First, we discuss the space encompassed by fuzzy versions of an image and then, under certain assumptions, we provide a differential geometrical analysis of that space. More specifically, we create a subspace resulting from the convolution of an image with a complete set of orthonormal basis functions of a prespecified maximum size (which can represent an arbitrary scattered core within that size), and shows that the corresponding subspaces created from of a clean image and its blurred versions are equal under the ideal case of zero noise and some assumptions about the properties of the fuzzy cores.
 


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