" Evolutionary Eigenspace Learning using CCIPCA and IPCA for Face Recognition," ICCIT 2009 : WASET International Conference on Computer and Information Technology, Tokyo, Japan, May 27-29. World Academy of Science, Engineering and Technology, Vol:3 2009-05-25.
|Authors:||Ghazy M.R. Assassa, Mona F.M. Mursi, Hatim Aboalsamh|
|Full paper||Ghazy mohamed rateb Assassa_WASET_Tokyo_Paper_ID_JP41760_Evolutionary_CCIPCA_IPCA_4.2.pdf|
|Supplementary materials||Not Available|
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. Keywords— Candid covariance-free incremental principal components analysis (CCIPCA), face recognition, incremental principal components analysis (IPCA).