Single training sample palmprint recognition method based on wavelet subbands fusion
ZHANG Yan-qiang
LI Zhe-qian
WANG Bo-han
Abstract：In view of the poor performance of the present most palmprint recognition for single training sample system,a principal components analysis method for single training sample palmprint recognition was presented,which combined multi-subbands of wavelet transformation.This method combined wavelet low frequency subband with horizontal and vertical subbands to identify.Low-pass filter was utilized to enhance the robustness of horizontal and vertical subbands,and the summation operator was used to fuse their matching scores.Experimental results showed that for single training sample palmprint recognition the average recognition rate of the proposed method was 89.93％,which was 6％ ～ 9％ higher than some of the traditional algorithms.
Keywords：wavelet decompositionPCAmatching score fusionsingle training sample palmprint recognition
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 88-94 )