Face recognition with local contourlet combined patterns
Yichuan Wang, Shilian Yu, Weifeng Li, Longbiao Wang, Qingmin Liao
Abstract
This paper proposes a novel face image descriptor called local contourlet combined patterns (LCCP), based on the Non-Subsampled Contourlet Transform (NSCT), for face recognition. NSCT is a multiresolution analysis tool and can capture image information at multiple scales, orientations, and frequency bands. To adapt to the NSCT filter bank, a new encoding method named mean-based contrast patterns (MCP) is presented. We apply LBP and MCP to different levels' NSCT coefficient images respectively and then combine them to obtain a robust representation. Futhermore, block-based kernel Fisher linear discriminant (BKFLD) is used to select the most discriminative feature sets. Face recognition experiments on FERET database demonstrate the effectiveness of our proposed approach.
BibTeX
@inproceedings{icassp2016_facerecognitionw,
title = {Face recognition with local contourlet combined patterns},
author = {Yichuan Wang and Shilian Yu and Weifeng Li and Longbiao Wang and Qingmin Liao},
booktitle = {ICASSP 2016},
year = {2016}
}