Skip to content

Advertisement

Open Access

The Complete Gabor-Fisher Classifier for Robust Face Recognition

EURASIP Journal on Advances in Signal Processing20102010:847680

https://doi.org/10.1155/2010/847680

Received: 2 December 2009

Accepted: 20 April 2010

Published: 24 May 2010

Abstract

This paper develops a novel face recognition technique called Complete Gabor Fisher Classifier (CGFC). Different from existing techniques that use Gabor filters for deriving the Gabor face representation, the proposed approach does not rely solely on Gabor magnitude information but effectively uses features computed based on Gabor phase information as well. It represents one of the few successful attempts found in the literature of combining Gabor magnitude and phase information for robust face recognition. The novelty of the proposed CGFC technique comes from (1) the introduction of a Gabor phase-based face representation and (2) the combination of the recognition technique using the proposed representation with classical Gabor magnitude-based methods into a unified framework. The proposed face recognition framework is assessed in a series of face verification and identification experiments performed on the XM2VTS, Extended YaleB, FERET, and AR databases. The results of the assessment suggest that the proposed technique clearly outperforms state-of-the-art face recognition techniques from the literature and that its performance is almost unaffected by the presence of partial occlusions of the facial area, changes in facial expression, or severe illumination changes.

Keywords

Face RecognitionPhase InformationGabor FilterIllumination ChangePartial Occlusion

Publisher note

To access the full article, please see PDF.

Authors’ Affiliations

(1)
Laboratory of Artificial Perception, Systems and Cybernetics, Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia

Copyright

© V. Štruc and N. Pavešić. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement