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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/15273

Title: Evolutionary Face Recognition Using Principle Component Analysis
Authors: Hatim A. Aboalsamh
Keywords: face recognition
Issue Date: 2009
Publisher: Egyptian Computer Science Journal
Abstract: 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.
URI: http://hdl.handle.net/123456789/15273
Appears in Collections:College of Computer and Information Sciences

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