A Blur-Robust Descriptor with Applications to Face Recognition

NANO SCIENTIFIC RESEARCH CENTRE PVT.LTD.,  AMEERPET, HYD

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JAVA PROJECTS LIST--2013

JAVA 2013 IEEE PAPERS

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.

Existing System:

            The effects of blur, which normally arise due to out-of-focus lens, atmospheric turbulence, and relative motion between the sensor and objects in the scene, is an important problem in image analysis applications such as face recognition. In recognition applications, existing approaches to handle the effects of blur can be classified as: 1) inverse methods based on deblurring and 2) direct methods based on invariants.

Proposed System:

            We derive the proposed blur invariant in Project. In Project, we study the utility of the invariant for the problem of recognizing faces under arbitrary blur by considering degradations due to spatially uniform (homogenous) blur and spatially varying blur. We discuss the non-euclidean nature of the space of blur-invariants and show that it can be studied as a Grassmann manifold. Experiments where we study the robustness of the invariant under different proportions of quantization noise and other intraclass facial variations such as lighting, alignment, and expression between the gallery and probe.

Software Requirement Specification

Software Specification

Operating System       :           Windows XP

Technology                 :           JAVA 1.6, OPENCV

Hardware Specification

Processor                     :           Pentium IV

RAM                           :           512 MB

Hard Disk                   :           80GB