Comparing Robustness of Pairwise and Multiclass Neural-Network Systems
  for Face Recognition
  Noise, corruptions and variations in face images can seriously hurt the
performance of face recognition systems. To make such systems robust,
multiclass neuralnetwork classifiers capable of learning from noisy data have
been suggested. However on large face data sets 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 improving the robustness of face
recognition. In our experiments this approach is shown to outperform the
multiclass neural-network system in terms of the predictive accuracy on the
face images corrupted by noise.
