Center-Symmetric Local Nonsubsampled Contourlet Transform Binary Pattern Histogram Sequence for Face Recognition

Abstract:

In order to alleviate the effect of illumination variations and improve the face recognition rate, this paper proposes a novel non-statistics based face representation method, which is called Center-Symmetric Local Nonsubsampled Contourlet Transform Binary Pattern Histogram Sequence (CS-LNBPHS). This method first applies NSCT to decompose a face image, and obtains NSCT coefficients in different scales and various orientations. Then, CS-LBP operator is used to get CS-LBP feature maps from NSCT coefficients. After that, feature maps are respectively divided into several blocks, the concatenated histogram, which are calculated over each block, are used as the face features. Experimental results on YaleB, ORL face databases show the validity of the proposed approach especially for illumination, face expression and position.

• Periodical: Applied Mechanics and Materials (Volumes 433-435)
• Pages: 405-411
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.433-435.405
• Online since: October 2013
• Authors: Rong Bing Huang*, Xiao Qun Liu
• Keywords: Center-Symmetric Local Binary Pattern, Face Recognition, Feature Extraction, Nonsubsampled Contourlet Transform (NSCT)

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