NANO SCIENTIFIC RESEARCH CENTRE
PVT.LTD., AMEERPET, HYD
WWW.NSRCNANO.COM, 09640648777, 09652926926
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
