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Local Polynomial Contrast Binary Patterns for Face Recognition
Zhen Xu1,2; Yinyan Jiang1,2; Yichuan Wang1,2; Yicong Zhou3; Weifeng Li1,2; Qingmin Liao1,2
Source PublicationNeurocomputing

We propose a novel face representation model, called the polynomial contrast binary patterns (PCBP), based on the polynomial filters, for robust face recognition. It is assumed that the discrete array of pixel values comes about by sampling an underlying smooth surface in an image. The proposed method efficiently estimates the underlying local surface information, which is approximately represented as linear projection coefficients of the pixels in a local patch. The decomposition using polynomial filters can capture rich image information at multiple orientations and frequency bands. This guarantees its robustness to illumination and expression variations. The weighting scheme embeds different discriminative powers of each filter response image. We also propose to carry out a subsequent Fisher linear Discriminant (FLD) on each decomposed image for dimension reduction of features. Our extensive experiments on the public FERET and LFW databases demonstrate that the non-weighted Polynomial contrast binary patterns performs better than most of methods and the weighting scheme further improves the recognition rates. WPCBP+FLD(CD) and WPCBP+FLD(HI) can achieve much competitive or even better recognition performance compared with the state-of-the-art face recognition methods

KeywordFace Recognition Polynomial Filters Local Binary Patterns Surface Fitting
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Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorWeifeng Li
Affiliation1.Department of Electronic Engineering/Graduate School at Shenzhen, University, Tsinghua, China
2.Shenzhen Key Laboratory of Information Science and Technology, Shenzhen, China
3.Department of Computer and Information Science, University of Macau, Macau, China
Recommended Citation
GB/T 7714
Zhen Xu,Yinyan Jiang,Yichuan Wang,et al. Local Polynomial Contrast Binary Patterns for Face Recognition[J]. Neurocomputing,2018.
APA Zhen Xu,Yinyan Jiang,Yichuan Wang,Yicong Zhou,Weifeng Li,&Qingmin Liao.(2018).Local Polynomial Contrast Binary Patterns for Face Recognition.Neurocomputing.
MLA Zhen Xu,et al."Local Polynomial Contrast Binary Patterns for Face Recognition".Neurocomputing (2018).
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