Improving the Robustness of GNP-PCA Using the Multiagent System.

In order to improve the robustness of Genetic Network Programming fuzzy data mining and PCA (GNP-PCA) based face recognition in the Gaussian and Salt&Pepper noisy testing environments, a GNP-based multi-agent system is constructed using GNP-PCA and multi-resolution analysis in this paper. In the proposed approach, the different scales of training images in the Laplacian pyramid are regarded as sub-environments and each GNP-PCA is performed as an agent in its corresponding environment. Face recognition is finally realized by maximizing the weighted average matching degrees of all the persons in the training database. Experimental results indicate that the proposed method has improved the robustness of GNP-PCA in the Gaussian and Salt&Pepper noisy testing environments considerably. (C) 2017 Elsevier B.V. All rights reserved.