A blur-robust descriptor with applications to face recognition

Raghuraman Gopalan, Sima Taheri, Pavan Turaga, Rama Chellappa

Research output: Contribution to journalArticlepeer-review

56 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number6127874
Pages (from-to)1220-1226
Number of pages7
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume34
Issue number6
DOIs
StatePublished - May 1 2012

Keywords

  • Blur
  • Convolution
  • Face recognition.
  • Grassmann manifold
  • Subspace

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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