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Random Subspace Two-Dimensional PCA for face recognition
The two-dimensional Principal Component Analysis (2DPCA) is a robust method in face recognition. Much recent research shows that the 2DPCA is more reliable than the well-known PCA method in recognising human face. However, in many cases, this method tends to be overfitted to sample data. In this paper, we proposed a novel method named random subspace two-dimensional PCA (RS-2DPCA), which combines the 2DPCA method with the random subspace (RS) technique. The RS-2DPCA inherits the advantages of both the 2DPCA and RS technique, thus it can avoid the overfitting problem and achieve high recognition accuracy. Experimental results in three benchmark face data sets -the ORL database, the Yale face database and the extended Yale face database B - confirm our hypothesis that the RS-2DPCA is superior to the 2DPCA itself.
History
Title of book
Advances in multimedia information processing--PCM 2007 : 8th Pacific Rim Conference on Multimedia, Hong Kong, China, December 11-14, 2007 : proceedingsSeries
Lecture notes in computer science ; 4810Chapter number
81Pagination
655 - 664Publisher
Springer-VerlagPlace of publication
Berlin, GermanyPublisher DOI
ISSN
0302-9743ISBN-13
9783540772545ISBN-10
3540772545Language
engPublication classification
B1.1 Book chapterCopyright notice
2007, Springer-Verlag Berlin HeidelbergExtent
98Editor/Contributor(s)
H Horace, O Au, H Leung, M Sun, W Ma, S HuUsage metrics
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