(2D)2 FPCA: An Efficient Approach for Appearance Based Object Recognition
In this paper, a new technique called twodirectional two-dimensional Fisher principal component analysis ((2D)2FPCA) 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 2DFLD 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)2FPCA method. The introduced (2D)2FPCA 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.
principal component analysis linear discriminant analysis 2DPCA plus 2DFLD(2D-FPCA) appearance based model object recognition Finger-Vein recognition
Yu Chengbo Qing Huafeng Zhang Lian
Research Institute of Remote Test & Control,Chongqing Institute of Technology,Chongqing,China
国际会议
北京
英文
1-4
2009-06-11（万方平台首次上网日期，不代表论文的发表时间）