Palmprint Recognition Based on Two-Dimensional Gabor Wavelet Transform and Two-Dimensional Principal Component Analysis

被引：0
|
作者：
Zhang, Yu [1 ]
Qi, Mei-Xing [2 ]
Shang, Li [1 ]
机构：
[1] Suzhou Vocat Univ, Depart Elect informat Engn, Suzhou Int Educ Pk,Rd 106, Suzhou 21504, Jiangsu, Peoples R China
[2] Suzhou Ind Pk Inst Vocat Technol, Dept Engn Mech, Suzhou 215021, Peoples R China
来源：
ADVANCED INTELLIGENT COMPUTING | 2011年 / 6838卷
关键词：
Two-Dimensional Gabor Wavelet Transform; Two-Dimensional Principal Component Analysis; eigenvector; The nearest neighbor classifier; Palmprint recognition;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A method of palmprint recognition based on Two-Dimensional Gabor(2DGabor) Wavelet Transform and Two-Dimensional Principal Component Analysis(2DPCA) is proposed. This method firstly carries out 2DGabor wavelet transform for the palmprint image, so that the amplitude of the filtered image can be used as the eigenvector for the palmprint image. And the 2DPCA is used to decrease the dimension of the eigenvector, and the nearest neighbor classifier is employed for palmprint classification. Finally, simulation experiment to compare the method of palmprint recognition based on 2DGabor wavelet and 2DPCA with the method based on Gabor wavelet and PCA is conducted with the assistance of the palmprint image database provided by Hongkong Polytechnic University. The experiment shows that the palmprint recognition method based on 2DGabor wavelet transform and 2DPCA is more effective.
引用
收藏
页码：405 / +
页数：2
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