Abstract
Gabor filters can extract multi-orientation and multiscale features from face images. Researchers have designed different ways to use the magnitude of the filtered results for face recognition: Gabor Fisher classifier exploited only the magnitude information of Gabor magnitude pictures (GMPs); Local Gabor Binary Pattern uses only the gradient information. In this paper, we regard GMPs as smooth surfaces. By completely describing the shape of GMPs, we get a face representation method called Gabor Surface Feature (GSF). First, we compute the magnitude, 1stand 2ndderivatives of GMPs, then binarize them and transform them into decimal values. Finally we construct joint histograms and use subspace methods for classification. Experiments on FERET, ORL and FRGC 1.0.4 database show the effectiveness of GSF.
Original language | English |
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Title of host publication | 1st Asian Conference on Pattern Recognition, ACPR 2011 |
Pages | 288-292 |
Number of pages | 5 |
DOIs | |
Publication status | Published - 1 Dec 2011 |
Event | 1st Asian Conference on Pattern Recognition, ACPR 2011 - Beijing, China Duration: 28 Nov 2011 → 28 Nov 2011 |
Conference
Conference | 1st Asian Conference on Pattern Recognition, ACPR 2011 |
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Country/Territory | China |
City | Beijing |
Period | 28/11/11 → 28/11/11 |
Keywords
- face recognition
- feature extraction
- Gabor
- Gabor surface feature
- histogram
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition