MapReduce Implementation of Face Recognition Based on HOG Feature and NSMD

Shi Zhao Li; Hao Feng Huang; Nan Feng Xiao

doi:10.4028/www.scientific.net/AMM.385-386.1572

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 385-386MapReduce Implementation of Face Recognition Based...

MapReduce Implementation of Face Recognition Based on HOG Feature and NSMD

Abstract:

There are some problems in the traditional face recognition: too sensitive to light, its classification is too simple, hard to apply to distributed system. For these problems, this paper does three work: used HOG feature in the face recognition, that improved it get more steady feature in different type of light; putting forward NSMD (Nearest Sample Max Distance), it try to find some partly best classifications to unite one strong classification, so the classification hyperplane can be more far from the sample boundary; finally the algorithms of this paper specially the one-to-one design mode are adapt to MapReduce, so its feasible to own the advantages of distributed system (expansively safety...). With the experiment analysis, the work gets their goals: HOG feature can be adapt to different lights, NSMD can get better rate of identification, the MapReduce algorithms can get fine speed-up ratio.

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Periodical:

Applied Mechanics and Materials (Volumes 385-386)

Pages:

1572-1575

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.385-386.1572

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Online since:

August 2013

Authors:

Shi Zhao Li, Hao Feng Huang, Nan Feng Xiao

Keywords:

Face Recognition, Histograms of Oriented Gradient (HOG), MapReduce, NSMD, Support Vector Machine (SVM)

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