Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/41679
Title: SIFT-PCA: An efficient and accurate face representation and recognition model
Authors: Shekar, B.H.
Sharmila Kumarr, M.
Shivakumara, P. 
Keywords: Eigenfaces
Face recognition
Fisherfaces
Linear discriminant analysis
Local descriptors
Principal component analysis
Issue Date: 2009
Source: Shekar, B.H.,Sharmila Kumarr, M.,Shivakumara, P. (2009). SIFT-PCA: An efficient and accurate face representation and recognition model. Proceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009 : 1969-1980. ScholarBank@NUS Repository.
Abstract: In this paper, a new technique called SIFT-PCA is devised for efficient and accurate face representation and recognition. Discriminative SIFT descriptors are extracted for compact representation. Unlike PCA-SIFT that employs PCA on normalized gradient patch, we employ PCA on smoothed weighted histograms. The proposed model has better computing performance in terms of recognition rate than the basic SIFT model. To establish the superiority of the proposed model, we have experimentally compared the performance of our new algorithm with recently proposed (2D)2-PCA on the benchmark databases: AT&T and UMIST face datasets. Copyright © 2009 by IICAI.
Source Title: Proceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009
URI: http://scholarbank.nus.edu.sg/handle/10635/41679
ISBN: 9780972741279
Appears in Collections:Staff Publications

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