基于流形学习及可拓分类器的手指静脉识别研究
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摘要
作为一种新的身份认证技术,手指静脉识别技术具有更高安全性和高精度,拥有广阔的应用前景。手指静脉识别技术主要是通过人体手指静脉的特点进行身份认证。针对手指静脉采集装置、手指静脉特征提取与分类器设计等内容,本文进行了相关研究。
首先,设计并制作一个手指静脉采集装置。根据文献资料所展示的手指静脉图像,分析国内已知的手指静脉采集实验装置存在的缺点,重新设计一个可以根据手指的厚薄,自动调整红外发射光强度,基于Windows USB2.0接口的手指静脉采集装置。该采集装置硬件部分包括手指静脉成像模块、自动调光模块、USB控制模块、其他辅助模块(触发按键控制、I2C总线模块和电源模块)；软件部分包括下位机USB外设的固件程序、上位机操作系统的USB驱动程序、上位机图像手指静脉采集程序。
其次,本文研究基于线性流形学习的手指静脉特征提取方法。详尽分析线性流形学习方法中典型的特征提取方法：主成分分析(PCA)、线性鉴别分析(LDA)、双向二维主成分分析(B2DPCA)和双向二维线性鉴别分析(B2DLDA)。在此基础上,着重分析传统的单方向加权模式存在的问题,提出了两种改进算法,双向加权B2DLDA(BWB2DLDA或(W2D)2LDA)和特征值归一化双向加权B2DPCA (BWB2DPCA或(OW2D)2PCA)。在分析模块化PCA的特点的基础上,结合模块化与双向加权两种方式的优点,提出一种新的改进算法,即在特征值归一化基础上,双向加权分块的B2DPCA (BWMB2DPCA)。并且通过大量的手指静脉对比实验验证提出算法优于传统算法。
然后,本文研究基于非线性流形学习的手指静脉特征提取方法。详尽分析局部线性嵌入(LLE)、等距映射(ISOMAP)和拉普拉斯特征映射(LE)这几种典型算法。但这些算法只能在训练样本集上形成映射,不能对新增的测试样本形成映射。然后又研究了局部保持映射(LPP)、有监督的局部保持映射(SLPP)和双向二维局部保持映射(B2DLPP)等算法,并在此基础上,提出了改进算法为双向加权分块的B2DLPP(BWM B2DLPP),并且通过手指静脉对比实验验证提出算法的有效性。
最后,本文研究基于可拓学的分类器方法。针对可拓学中可拓距和关联函数作深入研究,为了解决传统可拓分类器只适合用于模式类别较少的问题,提出了基于可拓距和关联函数的三种分类器设计方法,包括平均关联函数法、K最大关联函数法和最大关联函数法。采用包括手指静脉图像等3种类型实验数据进行对比分析,实验结果表明：基于可拓距与关联函数的分类器算法的分类效果,可以达到经典的K近邻法或最近邻法的分类水平。
As a new kind of identity authentication technology, finger vein identification technology has achieved more advantage of security and high-precision. Therefore, it has very broad application prospects. Authentication is through the body characteristics of the human finger vein. In this paper, the finger vein image collection system, feature extraction of finger vein and classifier are researched.
Firstly, An acquisition device of finger vein image was designed and then produced. According to the finger vein images of many literatures, the disadvantages of these finger vein acquisition devices that were designed in china are analyzed. In order to get consistency quality finger vein images, a new acquisition device of finger vein image was designed. According to the finger thickness, auto-dimming circuit is designed which can automatically adjust the intensity of the crossing infrared light and keep the image relatively stable. And the device'interface is Windows USB2.0. The device is designed based on USB2.0 and CMOS. Experimental results show that the finger vein image collected by this system can be clear and stable quality. The hardware is made of CMOS image sensor module, auto-dimming module, USB controller module, other supporting module and so on. The software is made of USB firmware program, USB driver and image acquisition program.
Secondly, finger vein feature extraction methods based on linear manifold learning are studied. The typical feature extraction methods of linear manifold learning are analyzed in detailed. The typical feature extraction methods are principal component analysis (PCA), linear discriminant analysis (LDA), bi-directional two dimensional PCA (B2DPCA) and bi-directional two dimensional LDA (B2DLDA). On the basis, the shortages of the traditional weighted method with one directional are analyzed, and two improved algorithms are proposed. The two algorithms are bi-directional weighted B2DLDA (BWB2DLDA or (W2D)2LDA) and bi-directional weighted B2DPCA (BWB2DPCA or (W2D)2PCA) with eigenvalue normalization. The characteristics of modular PCA are analyzed. And combining the advantages of the image modular and the bi-directional weighted, a new improved algorithm is proposed, which is bi-directional weighted modular B2DPCA (BWMB2DPCA) with eigenvalue normalization. The comparing experimental results of finger vein show that the proposed algorithms are better than the traditional algorithms.
Thirdly, finger vein feature extraction methods based on nonlinear manifold learning are studied. The typical feature extraction methods of nonlinear manifold learning are analyzed in detailed. The typical feature extraction methods are locally linear embedding (LLE), isometric mapping (ISOMAP), Laplacian Eigenmap (LE). However, these algorithms can only formed mapping in the training samples, and the mapping in a new test sample can not be formed. So locality preserving projections (LPP), supervised locality preserving projection (SLPP) and bi-directional two dimensional LPP (B2DLPP) are studied. On the basis, a new improved algorithm is proposed. The algorithm is bi-directional weighted modular B2DLPP (BWMB2DLPP). The comparing experimental results of finger vein show the validity of the algorithm.
Finally, the classifiers based on extension are studied. The extension distance and the correlation function of extension are deeply studied. In order to solve the problem that the traditional extension classifier is only suitable for little pattern, three classifiers based on the extension distance and the extension correlation function are proposed. These classifiers are average correlation function, K-maximum correlation function and maximum correlation function. Including finger vein images, three kinds of experimental data are used to compare and analyze for the extension classifiers. The test results show that the classification results of the extension classifiers can reach the level of k-nearest neighbor or nearest neighbor.
首先,设计并制作一个手指静脉采集装置。根据文献资料所展示的手指静脉图像,分析国内已知的手指静脉采集实验装置存在的缺点,重新设计一个可以根据手指的厚薄,自动调整红外发射光强度,基于Windows USB2.0接口的手指静脉采集装置。该采集装置硬件部分包括手指静脉成像模块、自动调光模块、USB控制模块、其他辅助模块(触发按键控制、I2C总线模块和电源模块)；软件部分包括下位机USB外设的固件程序、上位机操作系统的USB驱动程序、上位机图像手指静脉采集程序。
其次,本文研究基于线性流形学习的手指静脉特征提取方法。详尽分析线性流形学习方法中典型的特征提取方法：主成分分析(PCA)、线性鉴别分析(LDA)、双向二维主成分分析(B2DPCA)和双向二维线性鉴别分析(B2DLDA)。在此基础上,着重分析传统的单方向加权模式存在的问题,提出了两种改进算法,双向加权B2DLDA(BWB2DLDA或(W2D)2LDA)和特征值归一化双向加权B2DPCA (BWB2DPCA或(OW2D)2PCA)。在分析模块化PCA的特点的基础上,结合模块化与双向加权两种方式的优点,提出一种新的改进算法,即在特征值归一化基础上,双向加权分块的B2DPCA (BWMB2DPCA)。并且通过大量的手指静脉对比实验验证提出算法优于传统算法。
然后,本文研究基于非线性流形学习的手指静脉特征提取方法。详尽分析局部线性嵌入(LLE)、等距映射(ISOMAP)和拉普拉斯特征映射(LE)这几种典型算法。但这些算法只能在训练样本集上形成映射,不能对新增的测试样本形成映射。然后又研究了局部保持映射(LPP)、有监督的局部保持映射(SLPP)和双向二维局部保持映射(B2DLPP)等算法,并在此基础上,提出了改进算法为双向加权分块的B2DLPP(BWM B2DLPP),并且通过手指静脉对比实验验证提出算法的有效性。
最后,本文研究基于可拓学的分类器方法。针对可拓学中可拓距和关联函数作深入研究,为了解决传统可拓分类器只适合用于模式类别较少的问题,提出了基于可拓距和关联函数的三种分类器设计方法,包括平均关联函数法、K最大关联函数法和最大关联函数法。采用包括手指静脉图像等3种类型实验数据进行对比分析,实验结果表明：基于可拓距与关联函数的分类器算法的分类效果,可以达到经典的K近邻法或最近邻法的分类水平。
As a new kind of identity authentication technology, finger vein identification technology has achieved more advantage of security and high-precision. Therefore, it has very broad application prospects. Authentication is through the body characteristics of the human finger vein. In this paper, the finger vein image collection system, feature extraction of finger vein and classifier are researched.
Firstly, An acquisition device of finger vein image was designed and then produced. According to the finger vein images of many literatures, the disadvantages of these finger vein acquisition devices that were designed in china are analyzed. In order to get consistency quality finger vein images, a new acquisition device of finger vein image was designed. According to the finger thickness, auto-dimming circuit is designed which can automatically adjust the intensity of the crossing infrared light and keep the image relatively stable. And the device'interface is Windows USB2.0. The device is designed based on USB2.0 and CMOS. Experimental results show that the finger vein image collected by this system can be clear and stable quality. The hardware is made of CMOS image sensor module, auto-dimming module, USB controller module, other supporting module and so on. The software is made of USB firmware program, USB driver and image acquisition program.
Secondly, finger vein feature extraction methods based on linear manifold learning are studied. The typical feature extraction methods of linear manifold learning are analyzed in detailed. The typical feature extraction methods are principal component analysis (PCA), linear discriminant analysis (LDA), bi-directional two dimensional PCA (B2DPCA) and bi-directional two dimensional LDA (B2DLDA). On the basis, the shortages of the traditional weighted method with one directional are analyzed, and two improved algorithms are proposed. The two algorithms are bi-directional weighted B2DLDA (BWB2DLDA or (W2D)2LDA) and bi-directional weighted B2DPCA (BWB2DPCA or (W2D)2PCA) with eigenvalue normalization. The characteristics of modular PCA are analyzed. And combining the advantages of the image modular and the bi-directional weighted, a new improved algorithm is proposed, which is bi-directional weighted modular B2DPCA (BWMB2DPCA) with eigenvalue normalization. The comparing experimental results of finger vein show that the proposed algorithms are better than the traditional algorithms.
Thirdly, finger vein feature extraction methods based on nonlinear manifold learning are studied. The typical feature extraction methods of nonlinear manifold learning are analyzed in detailed. The typical feature extraction methods are locally linear embedding (LLE), isometric mapping (ISOMAP), Laplacian Eigenmap (LE). However, these algorithms can only formed mapping in the training samples, and the mapping in a new test sample can not be formed. So locality preserving projections (LPP), supervised locality preserving projection (SLPP) and bi-directional two dimensional LPP (B2DLPP) are studied. On the basis, a new improved algorithm is proposed. The algorithm is bi-directional weighted modular B2DLPP (BWMB2DLPP). The comparing experimental results of finger vein show the validity of the algorithm.
Finally, the classifiers based on extension are studied. The extension distance and the correlation function of extension are deeply studied. In order to solve the problem that the traditional extension classifier is only suitable for little pattern, three classifiers based on the extension distance and the extension correlation function are proposed. These classifiers are average correlation function, K-maximum correlation function and maximum correlation function. Including finger vein images, three kinds of experimental data are used to compare and analyze for the extension classifiers. The test results show that the classification results of the extension classifiers can reach the level of k-nearest neighbor or nearest neighbor.
引文
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