基于2DPCA和EBFNN的指纹识别方法  

A novel fingerprint recognition algorithm based on 2DPCA and EBFNN

作　　者：罗菁[1] 林树忠[2] 詹湘琳[3] 倪建云[4] 

机构地区：[1]天津工业大学计算机技术与自动化学院,天津300160 [2]天津市现代机电装备技术重点实验室,天津300160 [3]中国民航大学电子信息工程学院,天津300300 [4]天津理工大学自动化学院,天津300191

出　　处：《光学精密工程》2008年第9期1773-1780,共8页Optics and Precision Engineering

基　　金：国家自然科学基金重点项目(No.60534050)

摘　　要：结合小波变换(WT)、二维主元分析(2DPCA)和椭球基函数(EBF)特点,提出了一种基于WT、2DPCA和EBF神经网络指纹识别方法。利用小波变换将原始图像分解为高频分量和低频分量,并忽略水平高频与垂直高频分量,获得原始图像的基本特征。再通过2DPCA算法对该图像进行降维,获取降维特征;最后结合椭球基函数神经网络(EBFNN)完成指纹识别。本算法将2DPCA优化的特征提取与EBFNN的自适应性相结合,在FVC2000(国际指纹竞赛数据库)上做了测试,总的正确识别率可达91.4%,具有一定的实用价值。与WT-PNN算法和WT-2DPCA-RBF算法进行比较,结果表明,本文提出的算法在平移、旋转及光照变化的指纹数据库上的识别效果优于WT-PNN算法和WT-2DPCA-RBF算法。In combination with Wavelet Transform （WT）,Two-dimensional Principal Component Analysis（2DPCA） and Ellipsoidal Basis Function（EBF）, a fingerprint recognition algorithm based on WT, 2DPCA and EBF neural network（EBFNN） is proposed. Original images are decomposed into high-frequency and low-frequency components with WT, and horizontal and vertical high-frequency components are ignored, so the prime features of original images can be obtained;then, the projected features are solved by 2DPCA; finally, fingerprint recognition can be realized by EBFNN. The algorithm combines the optimization of 2DPCA and the adaptability of EBFNN and achieves the accurate recognition rate of 91.4%. The experimental results based on FVC2000 verify that proposed algorithm has higher recognition rate than that of WT-PNN and WT-2DPCA-RBF.

关 键 词：指纹识别 二维主元分析 椭球基函数 小波变换 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]