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Home / programming language / Online Matlab Projects / Blur and Illumination Robust Face Recognition via Set-Theoretic Characterization
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Blur and Illumination Robust Face Recognition via Set-Theoretic Characterization

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SKU: PROJ1775 Categories: Final Year Projects, 2013 Projects, Digital Image Processing Projects, 2014 Projects, Online Matlab Projects Tags: academic projects, Android project 2013-2014, Android project Abstract, Android project list, btech projects, dotnet project 2013-2014, dotnet project Abstract, dotnet project list, Final Year Projects, Final Year Projects for Computer Science, ieee abstracts, ieee paper download, ieee projects 2013-2014, ieee titles, java project list, java projects 2013-2014, java projects Abstract, madurai software company, matlab project 2013-2014, matlab project Abstract, matlab project list, mtech projects, phd research work, Php project 2013-2014, Php project Abstract, Php project list, Power Electronic project 2013-2014, Power Electronic project Abstract, Power Electronic project list, project center Bangalore, project center chennai, project center Cochin, project center coimbatore, project center Kollam, project center madurai, project center ramnad, project center trichy, research center, Students Projects, Vlsi project 2013-2014, Vlsi project Abstract, Vlsi project list
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Blur and Illumination Robust Face Recognition via Set-Theoretic Characterization. We address the problem of unconstrained face recognition from remotely acquired images. The main factors that make this problem challenging are image degradation due to blur, and appearance variations due to illumination and pose. In this paper, we address the problems of blur and illumination. We show that the set of all images obtained by blurring a given image forms a convex set. Based on this set-theoretic characterization, we propose a blur-robust algorithm whose main step involves solving simple convex optimization problems. We do not assume any< Final Year Projects > parametric form for the blur kernels, however, if this information is available it can be easily incorporated into our algorithm. Furthermore, using the low-dimensional model for illumination variations, we show that the set of all images obtained from a face image by blurring it and by changing the illumination conditions forms a bi-convex set. Based on this characterization, we propose a blur and illumination-robust algorithm. Our experiments on a challenging real dataset obtained in uncontrolled settings illustrate the importance of jointly modeling blur and illumination.

Abstract—Blur and Illumination Robust Face Recognition via Set-Theoretic Characterization.

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