摘要
在掌纹识别问题的研究中,首先在频域内对图像在主方向上利用2D Gabor滤波器进行滤波,增强特征纹线信息。然后通过小波变换对掌纹图像进行分解,可以降低图像的分辨率并提取低频成份。对二维主成分分析(2DPCA)可以降低计算复杂度,有利于计算掌纹图像的特征。在样本采集过程中难免会有一些由于微小旋转或挤压所引起的噪声所带来的影响,为了对传统的2DPCA算法进行改进,并提高掌纹算法的识别率。同时利用减少上述噪声的影响。将两种方法结合在一起,反复进行掌纹特征的计算,最后使用最近邻法则进行匹配。实验表明,矩不变量配合2DPCA的方法可以提高掌纹图像的识别率。
This paper proposed an enhanced algorithm of palmprint recognition.The 2D Gabor was done firstly to filter in the main direction and strengthen the primary line＇s information.Then we adopted wavelet transform to decompose the palmprint image,and we can decrease the resolution and extract the low frequency component.2-Dimentional Principal Component Analysis（2DPCA） can avoid transforming from image matrix to 1D vector so as to reduce the computational complexity and gain the eigenvalue of image.However,some noises will affect the algorithm due to the tiny rotation and squeezing in the samples collection.In order to improve the traditional 2DPCA,and increase the recognition rate of palmprints,the paper applied the Moment invariance.It is not sensitive to the noise mentioned above,and can prevent from being influenced by them.This paper combined the two methods,and calculated the eigenvalue again and again,then matched each other by nearest distance rule.The experiment demonstrates that 2DPCA combining with moment invariances can improve recognition rate compare to 2DPCA.
出处
《计算机仿真》
CSCD
北大核心
2010年第10期197-201,293,共6页
Computer Simulation
基金
广东省自然科学基金(06300098)
关键词
小波分解
二维主成分分析
矩不变量
Wavelet decomposition
2-Dimentional principal component analysis（2DPCA）
Moment invariance
