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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Learning the best subset of local features for face recognition
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Learning the best subset of local features for face recognition

机译:学习人脸识别的最佳局部特征子集

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摘要

We propose a novel, local feature-based face representation method based on two-stage subset selection where the first stage finds the informative regions and the second stage finds the discriminative features in those locations. The key motivation is to learn the most discriminative regions of a human face and the features in there for person identification, instead of assuming a priori any regions of saliency. We use the subset selection-based formulation and compare three variants of feature selection and genetic algorithms for this purpose. Experiments on frontal face images taken from the FERET dataset confirm the advantage of the proposed approach in terms of high accuracy and significantly reduced dimensionality. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
机译:我们提出了一种基于两阶段子集选择的新颖,基于局部特征的人脸表示方法,其中第一阶段找到信息性区域,第二阶段找到那些位置的区分性特征。关键动机是要学习人脸的最有区别的区域及其中的特征以供人识别,而不是先验地假定显着性的任何区域。我们使用基于子集选择的公式,并为此目的比较了特征选择和遗传算法的三个变体。从FERET数据集中获取的正面人脸图像的实验证实了该方法在高精度和大幅降低尺寸方面的优势。 (c)2006模式识别学会。由Elsevier Ltd.出版。保留所有权利。

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