基于多角度旋转积分图的手背静脉身份识别
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摘要
随着生物特征识别技术水平的飞速发展,手背静脉身份识别也广泛运用于各个领域;由于采集终端硬件设备和采集环境的差异,手背静脉识别效果并不理想;针对手背静脉图像在亮度,旋转,尺寸等方面造成的影响,提出了基于多角度旋转积分图的手背静脉身份识别方法;首先在尺度归一化后结合检测边缘性能的静脉图像梯度分割方法对图像进行二值分割,然后选取最佳角度间隔做旋转积分运算,最后通过二维离散余弦变换截取最佳特征矩阵用于分类识别,识别率超过99.9%;通过对比其它传统算法对手背静脉图像的识别效果来验证本文特征提取方法的可行性和优越性。
With the rapid development of the biometric technology,the identity recognition technology of dorsal hand vein has been more and more widely used in many fields.Because of the influence of the different hardware conditions and environment,the recognition rate is not ideal.Study on the problem of brightness,rotation,scales of the dorsal hand vein images,a method of dorsal hand vein identity recognition based on multi-angle rotation integration is proposed.At first,the dorsal hand vein images are binary-segmented by agradient method of detecting edge performance after scale normalized,then select the best angular interval for the rotation integral operation,finally,the best feature matrix is intercepted after two-dimensional discrete cosine transform for classification and recognition,the recognition rate is more than 99.9%.The experiment verifies the feasibility and superiority of the method proposed in this paper by comparing the recognition effects of other traditional algorithm of dorsal hand vein images.
With the rapid development of the biometric technology,the identity recognition technology of dorsal hand vein has been more and more widely used in many fields.Because of the influence of the different hardware conditions and environment,the recognition rate is not ideal.Study on the problem of brightness,rotation,scales of the dorsal hand vein images,a method of dorsal hand vein identity recognition based on multi-angle rotation integration is proposed.At first,the dorsal hand vein images are binary-segmented by agradient method of detecting edge performance after scale normalized,then select the best angular interval for the rotation integral operation,finally,the best feature matrix is intercepted after two-dimensional discrete cosine transform for classification and recognition,the recognition rate is more than 99.9%.The experiment verifies the feasibility and superiority of the method proposed in this paper by comparing the recognition effects of other traditional algorithm of dorsal hand vein images.
引文
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[15]孙继平,刘剑桥.基于离散余弦变换低频分量特征及学习向量量化的煤岩识别方法[J].工矿自动化,2015,41(11):1-6.
[16]光俊叶,刘明霞,张道强.基于有效距离的谱聚类算法[J].计算机科学与探索,2014,8(11):1365-1372.
[2]王一丁.手背静脉身份识别技术[M].北京:科学出版社,2015.
[3]Standring S.Grays anatomy,39th ed[M].Edinburgh:Elsevier Churchill Livingston,2005.
[4]樊云,方磊,樊磊.触控与手背静脉识别应用探讨[J].电视技术,2014,38(s2).
[5]Im S K,Park H M,Kim Y W,et al.A biometric identification system by extracting hand vein patterns[J].Journal of the Korean Physical Society,2001,38(3):268-272.
[6]杨晨艳.手背静脉图像容量研究与分析[D].北京:北方工业大学,2016.
[7]王一丁,王聪聪.基于多模板融合的异质手背静脉身份识别[J].计算机应用研究,2017,34(3):928-932.
[8]Niblack W.An introduction to image processing[M].Prentice-Hall,1986:115-116.
[9]Sauvola J,Pietikainen M.Adaptive document image binarization[J].Pattern Recognition,2000,33(2):225-236.
[10]Ding Y,Zhuang D,Wang K.A study of hand vein recognition method[A].Proceedings of the IEEE International Conference on Mechatronics&Automation[C].2005:2106-2110.
[11]Ohtsu N.A threshold selection method from gray-Level histograms[J].IEEE Transactions on Systems,Man&Cybernetics,2007,9(1):62-66.
[12]Wang Y,Wang H.Gradient based image segmentation for vein pattern[M].IEEE,2009.
[13]刘书桂,杨芳,邹志华,等.计算几何在测试计量技术中的应用---求解最大内接圆[J].仪器仪表学报,1998,19(6):576-580.
[14]邵平,杨路明.旋转45°矩形窗口的积分图像算法改进及应用[J].计算机应用与软件,2008,25(3):68-69.
[15]孙继平,刘剑桥.基于离散余弦变换低频分量特征及学习向量量化的煤岩识别方法[J].工矿自动化,2015,41(11):1-6.
[16]光俊叶,刘明霞,张道强.基于有效距离的谱聚类算法[J].计算机科学与探索,2014,8(11):1365-1372.