Abstract: This paper work addresses 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 parametric form for the blur kernels; however, if this information is available it can be easily incorporated into our algorithm. Further, using the SVM classification 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.
Keywords: SVM classification, Blur And Illumination Robust Face Recognition Using Set Theory.
Title: Blur and Illumination Robust Face Recognition Using Set Theory and SVM
Author: M. Jothi Padmapriya, R.Madhu Bala, M.Manjula, V. Karthikeyan
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
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