Blur And Illumination Robust Face Recognition VIA Set-Theoretic Characterization

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dc.contributor.advisorChellappa, Ramalingamen_US
dc.contributor.authorVageeswaran, Priyanka Sathiaen_US
dc.contributor.departmentElectrical Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2013-07-04T05:33:12Z
dc.date.available2013-07-04T05:33:12Z
dc.date.issued2013en_US
dc.description.abstractWe 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 problem 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 parametric form for the blur kernels, however, if this information is available, it can be easily incorporated into our algorithm. Further, 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.en_US
dc.identifier.urihttp://hdl.handle.net/1903/14302
dc.subject.pqcontrolledElectrical engineeringen_US
dc.subject.pqcontrolledComputer scienceen_US
dc.subject.pquncontrolledblurred and illuminated facesen_US
dc.subject.pquncontrolleddirect recognitionen_US
dc.subject.pquncontrolledremote biometricsen_US
dc.subject.pquncontrolledunconstrained face recognitionen_US
dc.titleBlur And Illumination Robust Face Recognition VIA Set-Theoretic Characterizationen_US
dc.typeThesisen_US

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