Paper Info
Title
Local Polynomial Contrast Binary Patterns for Face Recognition
Abstract
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.
Year
DOI
Venue
2019
10.1016/j.neucom.2018.09.056
Neurocomputing
Keywords
Field
DocType
Face recognition,Polynomial filters,Local binary patterns,Surface fitting
Facial recognition system,Weighting,Dimensionality reduction,Polynomial,Pattern recognition,Projection (linear algebra),Robustness (computer science),Pixel,Artificial intelligence,Linear discriminant analysis,Mathematics
Journal
Volume
ISSN
Citations 
355
0925-2312
0
PageRank 
References 
Authors
0.34
30
6
Name
Order
Citations
PageRank
Zhen Xu15018.31
Jiang Y2142.63
Yichuan Wang37411.12
Yicong Zhou41822108.83
Li W512712.48
QM646472.05