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Learning Multi-scale Block Local Binary Patterns for Face Recognition

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Learning Multi-scale Block Local Binary Patterns for Face Recognition
Abstract. In this paper, we propose a novel representation, called Multiscale Block Local Binary Pattern (MB-LBP), and apply it to face recognition. The Local Binary Pattern (LBP) has been proved to be effective for image representation, but it is too local to be robust. In MB-LBP, the computation is done based on average values of block subregions, instead of individual pixels. In this way, MB-LBP code presents several advantages: (1) It is more robust than LBP; (2) it encodes not only microstructures but also macrostructures of image patterns, and hence provides a more complete image representation than the basic LBP operator; and (3) MB-LBP can be computed very efficiently using integral images. Furthermore, in order to reflect the uniform appearance of MB-LBP, we redefine the uniform patterns via statistical analysis. Finally, AdaBoost learning is applied to select most effective uniform MB-LBP features and construct face classifiers. Experiments on Face Recognition Grand Chal...
ShengCai Liao, XiangXin Zhu, Zhen Lei, Lun Zhang,
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where ICB
Authors ShengCai Liao, XiangXin Zhu, Zhen Lei, Lun Zhang, Stan Z. Li
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