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PCM
2007
Springer

Random Subspace Two-Dimensional PCA for Face Recognition

9 years 4 months ago
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.
Nam Nguyen, Wanquan Liu, Svetha Venkatesh
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Where PCM
Authors Nam Nguyen, Wanquan Liu, Svetha Venkatesh
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