Gabor Surface Feature for face recognition

Ke Yan; Youbin Chen; Dapeng Zhang

doi:10.1109/ACPR.2011.6166553

Gabor Surface Feature for face recognition

Ke Yan, Youbin Chen, Dapeng Zhang

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

28 Citations (Scopus)

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
Title of host publication	1st Asian Conference on Pattern Recognition, ACPR 2011
Pages	288-292
Number of pages	5
DOIs	https://doi.org/10.1109/ACPR.2011.6166553
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
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

More information

10.1109/ACPR.2011.6166553

Cite this

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title = "Gabor Surface Feature for face recognition",

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.",

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AU - Chen, Youbin

AU - Zhang, Dapeng

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AB - 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.

KW - face recognition

KW - feature extraction

KW - Gabor

KW - Gabor surface feature

KW - histogram

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M3 - Conference article published in proceeding or book

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BT - 1st Asian Conference on Pattern Recognition, ACPR 2011

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ER -

Gabor Surface Feature for face recognition

Abstract

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Keywords

ASJC Scopus subject areas

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