K2DPCA plus 2DPCA: An Efficient Approach for Appearance Based Object Recognition
In this paper, we propose a new object recognition algorithm called two-directional twodimensional Kernel-based principal component analysis(K2DPCA plus 2DPCA). This approach mainly analyzes the object in the two dimensional principal component analysis (2DPCA) transformed space. Firstly, decorrelation in the row direction of images by through the standard K2DPCA method, then using 2DPCA way to further decorrelation in the column direction of images in the K2DPCA subspace. To overcome the shortcoming of massive memory requirements of the 2DPCA and 2DFPCA, we introduce K2DPCA plus 2DPCA method, which needs smaller memory space and has higher discernment rate, and computational efficiency is higher than the standard KPCA /K2DPCA/(2D)2FPCA method. Finally, we verify this method in the finger vein database.
Kernel-based methods principal component analysis linear discriminant analysis K2DPCA plus 2DPCA 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）