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W2051264368 abstract "A new technique called two- directional two-dimensional Fisher principal component analysis ((2D) 2 FPCA) in the two dimensional principal component analysis (2DPCA) transformed space is analyzed and its nature is revealed. We first argue that the standard 2D-PCA method works in the column direction of images and subsequently we propose an alternate 2D-FLD which works in the row direction of images in the 2DPCA subspace. To straighten out the problem of massive memory requirements of the 2D-PCA method and as well the alternate 2D-FPCA method, we introduce (2D) 2 FPCA method. The introduced (2D) 2 FPCA method has the advantage of higher recognition rate, lesser memory requirements and better computing performance than the standard PCA /2D-PCA /2D-FLD/2D-FPCA method, and the same has been revealed through extensive experimentations conducted on Finger-Vein dataset." @default.
W2051264368 created "2016-06-24" @default.
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W2051264368 date "2009-06-01" @default.
W2051264368 modified "2023-09-25" @default.
W2051264368 title "(2D)2 FPCA: an efficient approach for appearance based object recognition" @default.
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W2051264368 doi "https://doi.org/10.1109/icbbe.2009.5162133" @default.
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