Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15202
Title: Gabor Surface Feature for face recognition
Authors: Yan, K
Chen, Y
Zhang, D 
Keywords: Face recognition
Feature extraction
Gabor
Gabor surface feature
Histogram
Issue Date: 2011
Source: 1st Asian Conference on Pattern Recognition, ACPR 2011, 2011, p. 288-292 How to cite?
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, 1 st and 2 nd derivatives 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.
Description: 1st Asian Conference on Pattern Recognition, ACPR 2011, Beijing, 28 November 2011
URI: http://hdl.handle.net/10397/15202
ISBN: 9781457701221
DOI: 10.1109/ACPR.2011.6166553
Appears in Collections:Conference Paper

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