Combining 1D and 2D linear discriminant analysis for palmprint recognition
In this paper, a novel feature extraction method for palmprint recognition termed as Two-dimensional Combined Discriminant Analysis (2DCDA) is proposed. By connecting the adjacent rows of a image sequentially, the obtained new covariance matrices contain the useful information among local geometry structures in the image, which is eliminated by 2DLDA. In this way, 2DCDA combines LDA and 2DLDA for a promising recognition accuracy, but the number of coefficients of its projection matrix is lower than that of other two-dimensional methods. Experimental results on the CASIA palmprint database demonstrate the effectiveness of the proposed method.
Palmprint recognition dimensionality reduction Linear Discriminant Analysis Two-dimensional Linear Discriminant Analysis
Jian Zhang Hongbing Ji Lei Wang Lin Lin
School of Electronic Engineering, Xidian University, Xi’an, China
国际会议
桂林
英文
1-6
2011-11-01（万方平台首次上网日期，不代表论文的发表时间）