Skip to main content

Advertisement

You are viewing the new BMC article page. Let us know what you think. Return to old version

Research Article | Open | Published:

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

Abstract

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.

Publisher note

To access the full article, please see PDF.

Author information

Correspondence to V. Schetinin.

Rights and permissions

Reprints and Permissions

About this article

Keywords

  • Neural Network
  • Information Technology
  • Image Data
  • Quantum Information
  • Face Recognition