Abstract
In this paper, we present a novel approach to improve the performance of face recognition. To represent face images, we propose an effective texture descriptor, i.e., multi-scale ICA texture pattern (MITP). MITP generates multiple encoded images according to the order of response images by learned independent component analysis (ICA) filters of various scales, and then concatenates the MITP histograms from non-overlapping subregions of the encoded images into a single histogram. Based on a fundamental concept that a specific class can be modeled by a single query-dependent prototype, we introduce a simple classifier without parameter tuning, in which the decision is made using the farthest prototype rule. Moreover, a simple feature remapping strategy can further boost the performance. Experiments on two widely-used face databases demonstrate the effectiveness of our approach over other methods.
Published Version
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