The document proposes a method for face recognition in the presence of non-uniform motion blur from hand-held cameras. It models a blurred face as a convex combination of geometrically transformed gallery images. It develops an algorithm using the assumption of sparse camera motion and an l1-norm constraint. The framework is extended to handle illumination variations by exploiting the bi-convex set of images from blurring and illumination changes. The method is also extended to account for pose variations and uses a multi-scale implementation for efficient computation and memory usage.

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Face Recognition Across Non-Uniform Motion Blur,
Illumination, and Pose
ABSTRACT:
Existing methods for performing face recognition in thepresence of blur are based on theconvolution model and cannot handle
non-uniform blurring situations that frequently arise from tilts and rotations in hand-held cameras. In this paper, we proposea
methodology for face recognition in the presence of space-varying motion blur comprising of arbitrarily-shaped kernels. We
model the blurred face as a convex combination of geometrically transformed instances of the focused gallery face, and show that
the set of all images obtained by non-uniformly blurring a given image forms a convex set. We first propose anon-uniform blur-
robust algorithm by making use of theassumption of a sparsecamera trajectory in the camera motion spaceto build an energy
function with l1-norm constraint on the camera motion. Theframework is then extended to handle illumination variations by
exploiting thefact that the set of all images obtained from a face image by non-uniform blurring and changing the illumination
forms a bi-convex set. Finally, we proposean elegant extension to also account for variations in pose.
EXISTING SYSTEM:
 In common, blurring due to camera shake is modelled as convolution with single blur kernel and the blur is uniform across
the image this case is considered as space variant blur frequently in hand held cameras. Restoration of non-uniform blur is
based local spaceinvariant approximation and a recent methods for image restoration is motion-blurred image as an average
of projectively transformed images.
 Approaches to face recognition from blurred images can be broadly classified into four categories. (i) Deblurring-based in
which the probeimage is first deblurred and then used for recognition. However, deblurring artifacts are a major source of
error especially for moderate to heavy blurs. (ii) Joint deblurring and recognition, the flip-side of which is computational
complexity. (iii) Deriving blur-invariant features for recognition. But theseare effective only for mild blurs. (iv)
The direct recognition approach in which reblurred versions from the gallery are compared with theblurred probeimage.
 Patel et al.have proposed adictionary-based approach to recognizing faces across illumination and pose.
DISADVANTAGES OF EXISTING SYSTEM:
 Deblurring artifacts are a major source of error especially for moderate to heavy blurs.
 The flipside of which is computational complexity in joint and recognition

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 In deriving blur-invariant features is only effective for mild blurs.
 Although in subspacelearning approach it is difficult to solve the problems like blur,pose,illumination etc..
 A dictionary based approach, sparseminimization technique for recognizing faces with similar principles and offers
robustness to alignment and pose. But theseworks do not deal with blurred images.
PROPOSED SYSTEM:
 In this paper we are proposeaface recognition that is robust to non-uniform i.e space varying motion blur arising from
relative motion between the camera and thesubject.
 We will assume that only a single gallery image is available. The camera transformations can range from in-plane
translations and rotations to out-of-planetranslations, out-of-planerotations and even general 6D motion. Observe that the
blur on the faces can be significantly non-uniform.
 The simple yet restrictiveconvolution model fails to explain this blur and a space-varying formulation becomes necessary.
 We showed that the set of all images using the TSF model is a convex set given by theconvex hull of warped versions of
the image.
 We develop our basic non-uniform motion blur (NU-MOB)-robust facerecognition algorithm based on theTSF
(Transformation Spread Function) model.
ADVANTAGES OF PROPOSED SYSTEM:
 This proposed method of recognition allows us to circumvent the challenging and ill-posed problem of single image blind-
deblurring.
 It efficiently deals with blurred images.
 This is the first attempt to systematically address face recognition under (i) non-uniform motion blur and (ii) the combined
effects of blur, illumination and pose.
 We provethat the set of all images obtained by non-uniformly blurring a given image forms a convex set. We also show that
the set of all images obtained from a face image by non-uniform blurring and change of illumination forms a bi-convex set.
 We extend our method to non-frontal situations by transforming the gallery to a new pose.
 We proposeamulti-scale implementation that is efficient both in terms of computation as well as memory usage
SYSTEMARCHITECTURE:

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BLOCK DIAGRAM:

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SYSTEMREQUIREMENTS:
HARDWARE REQUIREMENTS:
 System: Pentium IV 2.4 GHz.
 Hard Disk : 40 GB.
 Floppy Drive: 44 Mb.
 Monitor : 15 VGA Colour.
 Mouse:
 Ram : 512 Mb.

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SOFTWARE REQUIREMENTS:
 Operating system: Windows XP/7.
 Coding Language : MATLAB
 Tool : MATLAB R2013A
REFERENCE:
Abhijith Punnappurath, Ambasamudram Narayanan Rajagopalan, Senior Member, IEEE, Sima Taheri, Student Member, IEEE,
Rama Chellappa, Fellow, IEEE, and Guna Seetharaman, Fellow, IEEE, “Face Recognition Across Non-Uniform Motion Blur,
Illumination, and Pose”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 24, NO. 7, JULY 2015.