Robust intelligent PCA‐based face recognition framework using GNP‐fuzzy data mining

Abstract

Traditional principal component analysis (PCA) based face recognition algorithms have a low recognition accuracy due to the influence of noise and illumination changes. This paper proposes a robust, intelligent PCA‐based face recognition framework in the complicated illumination database when using multiple training images per person (MTIP‐CID). There are mainly two improvements in the proposed method. One is that a face‐recognition‐oriented genetic‐based clustering algorithm is introduced to reduce the influence of a large number of classes on the classification accuracy in the MTIP‐CID. The other is that a classifier based on fuzzy class association rules (FCARs) is applied to mine the inherent relationships between eigenfaces and to improve the robustness of PCA‐based face recognition in noisy environments. Experimental results on the extended Yale‐B database demonstrate that the proposed framework performs better and is more robust against noise compared with other traditional face recognition algorithms, i.e. linear discriminant analysis (LDA) and local binary patterns (LBPs). © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

The Impact of Bio-Inspired Approaches Toward the Advancement of Face Recognition

“…Furthermore, the noisy situations are dynamic, which makes it difficult to train the model. In their recent work, Zhang et al [2011a] proposed the GNP-based multiagent GNP-MAS framework to overcome GNP-PCA drawbacks. Experimental results on the Yale B database demonstrate the robustness of GNP-MAS over GNP-PCA.…”

“…However, an RR of 92.5% is reported by applying the Leave-One-Out approach. Most recently, Ibrahem et al [2013] [Bozorgtabar et al 2010] ORL 63.5% 67.5% Leveraged GP [Bozorgtabar et al 2011] ORL 91.5% 92.5% GP [Ibrahem et al 2013] ORL 76% 98% GNP-PCA [Zhang et al 2011b] Yale-B 76% GNP-MAS [Zhang et al 2011a] Yale-B 78.07%…”