The Complete Gabor-Fisher Classifier for Robust Face Recognition
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Research Article The Complete Gabor-Fisher Classifier for Robust Face Recognition ˇ Vitomir Struc and Nikola Paveˇsi´c Laboratory of Artificial Perception, Systems and Cybernetics, Faculty of Electrical Engineering, University of Ljubljana, SI-1000 Ljubljana, Slovenia ˇ Correspondence should be addressed to Vitomir Struc, [email protected] Received 2 December 2009; Revised 15 April 2010; Accepted 20 April 2010 Academic Editor: Robert W. Ives ˇ Copyright © 2010 V. Struc and N. Paveˇsi´c. 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. 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.
1. Introduction Biometrics is a scientific discipline that uses unique and measurable physical, biological, or/and behavioral human characteristics that can be processed to establish identity, to perform identity verification, or to recognize a person through automation [1–3]. Among the different characteristics suitable for biometric recognition, the human face and the associated face recognition technology bear the most potential. This potential is fueled by the countless application possibilities of face recognition technology in the private as well as the public sector. Examples of potential application domains range from entertainment, humanmachine interaction, homeland security, smart surveillance, access and border control to user authentication schemes in e-commerce, e-health, and e-government services [1, 2, 4]. While, for example, access control applications can often ensure stable and controlled external conditions for the
image acquisition procedure, the majority of applications (especially those linked to unconstrained face recognition, e.g.,
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