Open Science Research Excellence
%0 Journal Article
%A Ghazy M.R. Assassa and  Mona F. M. Mursi and  Hatim A. Aboalsamh
%D 2009 
%J  International Journal of Computer, Electrical, Automation, Control and Information Engineering
%B World Academy of Science, Engineering and Technology
%I International Science Index 29, 2009
%T Evolutionary Eigenspace Learning using CCIPCA and IPCA for Face Recognition
%U http://waset.org/publications/12212
%V 29
%X Traditional principal components analysis (PCA)
techniques for face recognition are based on batch-mode training
using a pre-available image set. Real world applications require that
the training set be dynamic of evolving nature where within the
framework of continuous learning, new training images are
continuously added to the original set; this would trigger a costly
continuous re-computation of the eigen space representation via
repeating an entire batch-based training that includes the old and new
images. Incremental PCA methods allow adding new images and
updating the PCA representation. In this paper, two incremental
PCA approaches, CCIPCA and IPCA, are examined and compared.
Besides, different learning and testing strategies are proposed and
applied to the two algorithms. The results suggest that batch PCA is
inferior to both incremental approaches, and that all CCIPCAs are
practically equivalent.
%P 1292 - 1297