Maximizing all margins: Pushing face recognition with Kernel Plurality

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Maximizing all margins: Pushing face recognition with Kernel Plurality

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Title: Maximizing all margins: Pushing face recognition with Kernel Plurality
Author: Kumar, Ritwik; Banerjee, Arunava; Vemuri, Baba C.; Pfister, Hanspeter

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Citation: Kumar, Ritwik, Arunava Banerjee, Baba C. Vemuri, and Hanspeter Pfister. 2011. “Maximizing All Margins: Pushing Face Recognition with Kernel Plurality.” IEEE International Conference on Computer Vision: 2375-2382, Barcelona, Spain, November 6-13, 2011.
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Abstract: 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 plurality 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 can provide state-of-the-art results in terms of face recognition rates.
Published Version: doi:10.1109/iccv.2011.6126520
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:12374864
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