Curvelet变换结合(2D)~2PCA的人脸识别算法
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摘要
作为一种新的多尺度多方向性的信号分析工具,Curvelet变换不但具有小波变换多尺度和多分辨率的特点,还具有很强的方向性,对包含大量面部轮廓和五官曲线信息的人脸图像能实现最优的稀疏表示。本文提出并实现了一种基于Curvelet变换结合双向二维主成分分析((2D)~2PCA)的人脸识别算法,以Yale人脸数据库进行人脸识别实验,结果表明,该算法相对于传统基于小波变换的人脸识别算法,能有效提高识别率,缩短识别时间。
As a new tool for signal analysis with multi-scale and multi-direction,curvelet transform not only has multi-scale and multi-resolution characteristics of wavelet transform,but also has very strong directionality in the sense that it can provide an optimally sparse representation of face image with a large number of facial contours and five sense organs curve information.This paper proposes and realizes a face recognition method based on the second generation curvelet transform and two-directional two-dimensional principal component analysis.Experiments of face recognition based on Yale face database show that the method can effectively improve the recognition rate and shorten recognition time compare to the face recognition method based on traditional wavelet transform.
As a new tool for signal analysis with multi-scale and multi-direction,curvelet transform not only has multi-scale and multi-resolution characteristics of wavelet transform,but also has very strong directionality in the sense that it can provide an optimally sparse representation of face image with a large number of facial contours and five sense organs curve information.This paper proposes and realizes a face recognition method based on the second generation curvelet transform and two-directional two-dimensional principal component analysis.Experiments of face recognition based on Yale face database show that the method can effectively improve the recognition rate and shorten recognition time compare to the face recognition method based on traditional wavelet transform.
引文
[1]卢世军.生物特征识别技术发展与应用综述[J].计算机安全,2013(1):63-66.
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[4]CANDES E J,DONOHO D L.Curvelet-A Surprisingly Effective Nonadaptive Representation for Objects with Edges[A].In Curve and Surface Fitting:Saint-Malo1999[C].A Cohen C Rabut and L L Schumaker Eds Nashville TN:Van-derbilt Univ Press,1999.
[5]DONOHO D L,DUNCAN M R.Digital Curvelet Transform:Strategy,Implementation and Experiments[J].SPIE,2000,4056:12-29.
[6]CANDES E J,DEMANET L,DONOHO D,et al.Fast Discrete Curvelet Transforms[J].Multiscale Modeling&Simulation,2005,5(3):861-899.
[7]常颖,王艳春,刘丹.离散Curvelet变换在图像增强中的应用研究[J].长春理工大学学报(自然科学版),2012,35(3):131-133.
[8]ZHANG D Q,ZHOU Z H.(2D)2PCA:Two-directional Two-dimensional PCA for Efficient Face Representation and Recognition[J].Neurocomputing,2005,69:224-231.
[9]李新春,安秋艳.基于改进最近邻法的人脸识别系统研究[J].计算机应用与软件,2012,29(8):214-216.
[10]陈惠明.图像欧式距离在人脸识别中的应用研究[J].计算机工程与设计,2008,29(14):3735-3737.
[11]刘志明,周辉林.TR-MUSIC与图像熵相结合的室内多目标穿墙雷达图像重构[J].南昌大学学报(理科版),2015,39(4):338-341.
[2]李苗红,谷海红.人脸识别研究综述[J].电脑知识与应用,2011,7(24):5992-5994.
[3]曹雪.小波理论在人脸识别中的应用研究[D].南京:南京理工大学,2012.
[4]CANDES E J,DONOHO D L.Curvelet-A Surprisingly Effective Nonadaptive Representation for Objects with Edges[A].In Curve and Surface Fitting:Saint-Malo1999[C].A Cohen C Rabut and L L Schumaker Eds Nashville TN:Van-derbilt Univ Press,1999.
[5]DONOHO D L,DUNCAN M R.Digital Curvelet Transform:Strategy,Implementation and Experiments[J].SPIE,2000,4056:12-29.
[6]CANDES E J,DEMANET L,DONOHO D,et al.Fast Discrete Curvelet Transforms[J].Multiscale Modeling&Simulation,2005,5(3):861-899.
[7]常颖,王艳春,刘丹.离散Curvelet变换在图像增强中的应用研究[J].长春理工大学学报(自然科学版),2012,35(3):131-133.
[8]ZHANG D Q,ZHOU Z H.(2D)2PCA:Two-directional Two-dimensional PCA for Efficient Face Representation and Recognition[J].Neurocomputing,2005,69:224-231.
[9]李新春,安秋艳.基于改进最近邻法的人脸识别系统研究[J].计算机应用与软件,2012,29(8):214-216.
[10]陈惠明.图像欧式距离在人脸识别中的应用研究[J].计算机工程与设计,2008,29(14):3735-3737.
[11]刘志明,周辉林.TR-MUSIC与图像熵相结合的室内多目标穿墙雷达图像重构[J].南昌大学学报(理科版),2015,39(4):338-341.