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2011
IEEE

Maximizing All Margins: Pushing Face Recognition with Kernel Plurality

8 years 8 months ago
Maximizing All Margins: Pushing Face Recognition with Kernel Plurality
We present two theses in this paper: First, performance of most existing face recognition algorithms improves if instead of the whole image, smaller patches are individually classified followed by label aggregation using voting. Second, weighted plurality1 voting outperforms other popular voting methods if the weights are set such that they maximize the victory margin for the winner with respect to each of the losers. Moreover, this can be done while taking higher order relationships among patches into account using kernels. We call this scheme Kernel Plurality. We verify our proposals with detailed experimental results and show that our framework with Kernel Plurality improves the performance of various face recognition algorithms beyond what has been previously reported in the literature. Furthermore, on five different benchmark datasets - Yale A, CMU PIE, MERL Dome, Extended Yale B and Multi-PIE, we show that Kernel Plurality in conjunction with recent face recognition algorithms...
Ritwik Kumar, Arunava Banerjee, CISE, Univ, Baba V
Added 11 Dec 2011
Updated 11 Dec 2011
Type Journal
Year 2011
Where ICCV
Authors Ritwik Kumar, Arunava Banerjee, CISE, Univ, Baba Vemuri, Hanspeter Pfister
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