Sciweavers

ICPR
2008
IEEE

Ridge Regression for Two Dimensional Locality Preserving Projection

13 years 10 months ago
Ridge Regression for Two Dimensional Locality Preserving Projection
Two Dimensional Locality Preserving Projection (2DLPP) is a recent extension of LPP, a popular face recognition algorithm. It has been shown that 2D-LPP performs better than PCA, 2D-PCA and LPP. However, the computational cost of 2D-LPP is high. This paper proposes a novel algorithm called Ridge Regression for Two Dimensional Locality Preserving Projection (RR2DLPP), which is an extension of 2D-LPP with the use of ridge regression. RR-2DLPP is comparable to 2DLPP in performance whilst having a lower computational cost. The experimental results on three benchmark face data sets − the ORL, Yale and FERET databases − demonstrate the effectiveness and efficiency of RR2DLPP compared with other face recognition algorithms such as PCA, LPP, SR, 2D-PCA and 2D-LPP.
Nam Thanh Nguyen, Wanquan Liu, Svetha Venkatesh
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICPR
Authors Nam Thanh Nguyen, Wanquan Liu, Svetha Venkatesh
Comments (0)