基于MMNBP的手指静脉识别方法
|
摘要
针对传统的局部二值模式(LBP)手指静脉特征识别率不高的问题,提出基于多块均值近邻二值模式(MMNBP)的手指静脉识别方法。对LBP算法改进,提出基于近邻二值模式(NBP)的特征提取方法;将指静脉图像分块并取子块均值,对所有子块均值构成的图像采用NBP方法提取特征,从而形成MMNBP方法;利用汉明距离进行匹配。在国外和国内两个图库上与几种典型算法进行对比实验,结果表明,提出的方法可获得最低等误率分别为2. 4611%和0. 3137%,证明MMNBP方法能够进一步提高身份识别的鲁棒性,具有较好的稳定性和有效性。
In order to improve the problem that the traditional local binary pattern( LBP) of finger vein recognition rate is low,a finger vein identification method is proposed based on multi-block mean neighbors based binary pattern( MMNBP). LBP method is improved by proposing neighbor-based binary pattern( NBP). Finger vein image is evenly divided and extracting the features of all sub-block mean values by NBP,so as to form the MMNBP method. Match images by Hamming distance. Experiment databases are from the foreign and the domestic,and using several typical methods of identification for comparison experiments. The experimental results show that the method can get the lowest equal error rate are 2. 461 1 % and 0. 313 7 %,it is proved that the method can further improve the robustness of identity recognition,which has stability and effectiveness.
In order to improve the problem that the traditional local binary pattern( LBP) of finger vein recognition rate is low,a finger vein identification method is proposed based on multi-block mean neighbors based binary pattern( MMNBP). LBP method is improved by proposing neighbor-based binary pattern( NBP). Finger vein image is evenly divided and extracting the features of all sub-block mean values by NBP,so as to form the MMNBP method. Match images by Hamming distance. Experiment databases are from the foreign and the domestic,and using several typical methods of identification for comparison experiments. The experimental results show that the method can get the lowest equal error rate are 2. 461 1 % and 0. 313 7 %,it is proved that the method can further improve the robustness of identity recognition,which has stability and effectiveness.
引文
[1]王有春.指纹识别在身份证网络核查系统中的应用[J].传感器与微系统,2011,30(6):126-128.
[2]杨瑞,张云伟,苟爽,等. Gabor特征与深度信念网络结合的人脸识别方法[J].传感器与微系统,2017,36(5):68-70.
[3]孙君顶,刘晓惠,陈鹏鹏.基于改进局部方向模式的人脸识别[J].传感器与微系统,2016,35(6):52-55.
[4]苑玮琦,王黎黎,张宁宁.手部三模态图像采集系统的设计与实现[J].传感器与微系统,2013,32(5):78-81,85.
[5] YANG X,YANG C,YAO Z. The finger vein recognition based on shearlet[C]∥2016 the 8th International Conference on Wireless Communications&Signal Processing(WCSP),IEEE,2016:1-5.
[6] YU C B,TAN J,YU L,et al. A finger vein recognition method based on PCA-RBF neural network[J]. Hans Journal of Biomedicine,2012,2(4):23-27.
[7] PENG J L. Research on key issues of multi-modal biometric verification based on finger[D]. Harbin:Harbin Institute of Technology,2014:78-93.
[8]金怀国.基于特征点和子空间的手指静脉识别技术研究[D].北京:北京交通大学,2012.
[9] LEE E C,JUNG H,KIM D. New finger biometric method using near infrared imaging[J]. Sensors,2011,11(3):2319-2333.
[10] OJALA T,PIETIKAINEN M,MPENPAA T. Multiresolution grayscale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):971-987.
[11] ROSDI B A,SHING C W,SUANDI S A. Finger vein recognition using local line binary pattern[J]. Sensors,2011,11(12):11357-11371.
[12]杨文文,毛建旭.基于分块LBP和分块PCA的指静脉识别方法[J].电子测量与仪器学报,2016,30(7):1000-1007.
[13]王玲.基于LBP的特征提取研究[D].北京:北京交通大学,2009.
[14]徐天扬,惠晓威,林森,等.基于小波灰度曲面的近红外手指静脉识别方法[J].激光与光电子学进展,2016(4):88-96.
[15] ZHANG D,GUO Z H,LU G M,et al. Online joint palmprint and palmvein verification[J]. Expert Systems with Applications,2011,38(3):2621-2631.
[16] ASAARI M S M,SUANDI S A,ROSDI B A. Fusion of band limited phase only correlation and width centroid contour distance for finger based biometrics[J]. Expert Systems With Applications,2014,41(7):3367-3382.
[17] HABBAL A,BARELLI H,MALANDAIN G. Assessing the ability of the 2D Fisher-KPP equation to model cell-sheet wound closure[J].Mathematical Biosciences,2014,252:45-59.
[18]卢中宁,仲贞,贾桂敏,等.手指多模态Gabor编码特征局部融合方法研究[J].信号处理,2015,31(11):1467-1472.
[2]杨瑞,张云伟,苟爽,等. Gabor特征与深度信念网络结合的人脸识别方法[J].传感器与微系统,2017,36(5):68-70.
[3]孙君顶,刘晓惠,陈鹏鹏.基于改进局部方向模式的人脸识别[J].传感器与微系统,2016,35(6):52-55.
[4]苑玮琦,王黎黎,张宁宁.手部三模态图像采集系统的设计与实现[J].传感器与微系统,2013,32(5):78-81,85.
[5] YANG X,YANG C,YAO Z. The finger vein recognition based on shearlet[C]∥2016 the 8th International Conference on Wireless Communications&Signal Processing(WCSP),IEEE,2016:1-5.
[6] YU C B,TAN J,YU L,et al. A finger vein recognition method based on PCA-RBF neural network[J]. Hans Journal of Biomedicine,2012,2(4):23-27.
[7] PENG J L. Research on key issues of multi-modal biometric verification based on finger[D]. Harbin:Harbin Institute of Technology,2014:78-93.
[8]金怀国.基于特征点和子空间的手指静脉识别技术研究[D].北京:北京交通大学,2012.
[9] LEE E C,JUNG H,KIM D. New finger biometric method using near infrared imaging[J]. Sensors,2011,11(3):2319-2333.
[10] OJALA T,PIETIKAINEN M,MPENPAA T. Multiresolution grayscale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):971-987.
[11] ROSDI B A,SHING C W,SUANDI S A. Finger vein recognition using local line binary pattern[J]. Sensors,2011,11(12):11357-11371.
[12]杨文文,毛建旭.基于分块LBP和分块PCA的指静脉识别方法[J].电子测量与仪器学报,2016,30(7):1000-1007.
[13]王玲.基于LBP的特征提取研究[D].北京:北京交通大学,2009.
[14]徐天扬,惠晓威,林森,等.基于小波灰度曲面的近红外手指静脉识别方法[J].激光与光电子学进展,2016(4):88-96.
[15] ZHANG D,GUO Z H,LU G M,et al. Online joint palmprint and palmvein verification[J]. Expert Systems with Applications,2011,38(3):2621-2631.
[16] ASAARI M S M,SUANDI S A,ROSDI B A. Fusion of band limited phase only correlation and width centroid contour distance for finger based biometrics[J]. Expert Systems With Applications,2014,41(7):3367-3382.
[17] HABBAL A,BARELLI H,MALANDAIN G. Assessing the ability of the 2D Fisher-KPP equation to model cell-sheet wound closure[J].Mathematical Biosciences,2014,252:45-59.
[18]卢中宁,仲贞,贾桂敏,等.手指多模态Gabor编码特征局部融合方法研究[J].信号处理,2015,31(11):1467-1472.