Open Access

Comparing Robustness of Pairwise and Multiclass Neural-Network Systems for Face Recognition

EURASIP Journal on Advances in Signal Processing20072008:468693

Received: 16 June 2007

Accepted: 19 November 2007

Published: 6 December 2007


Noise, corruptions, and variations in face images can seriously hurt the performance of face-recognition systems. To make these systems robust to noise and corruptions in image data, multiclass neural networks capable of learning from noisy data have been suggested. However on large face datasets such systems cannot provide the robustness at a high level. In this paper, we explore a pairwise neural-network system as an alternative approach to improve the robustness of face recognition. In our experiments, the pairwise recognition system is shown to outperform the multiclass-recognition system in terms of the predictive accuracy on the test face images.


Neural NetworkInformation TechnologyImage DataQuantum InformationFace Recognition

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Authors’ Affiliations

Computing and Information System Department, University of Bedfordshire, Luton, UK


© J. Uglov et al. 2008

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.