脉冲耦合神经网络及其在指纹系统中的应用
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摘要
生物特征识别是一种利用人的生物特征,如指纹、掌纹、步态、声音和面相等特征,来进行身份识别的技术,而指纹则是各种生物特征中最典型、应用也最广泛的特征之一。常见的指纹识别系统会涉及到指纹增强、指纹方向场计算、特征提取、特征匹配和模式分类等技术。近几年来,尽管与指纹识别相关的技术和应用研究已经得到了长足的发展,但仍然还存在一些尚未被解决的问题,这使指纹识别系统的进一步推广受到了较大限制。目前仍然有许多科研机构都在积极探索指纹的新技术和新应用,取得了许多有价值的研究成果,这些成果将会使指纹在身份认证领域的应用前景更加广阔。
本文首先对传统的脉冲耦合神经网络(PCNN,Pulse Coupled Neural Network)进行了研究,提出了几种改进的模型如“M-PCNN”、“I-PCNN”和“TB-PCNN”等,在这些模型基础之上,为PCNN开发了一些新图像处理应用,如混合噪声滤除。本文对PCNN的高维形态进行了初步研究,设计了一种能够兼容传统二维网络的高维统一模型,此外还对这种模型的网络结构、神经元的连接输入、活性调制、脉冲发生规则等进行了讨论,并对高维网络的脉冲特性进行了试验仿真。
以各种类型的PCNN为基础,为指纹识别系统中的关键处理模块提出了一些新的处理算法,主要包括“指纹脊线增强”、“指纹的方向场估计”、“二值指纹图像细化”和“指纹模式分类”,并在这些基本的处理算法基础之上,展示了在研究过程中开发的一套指纹系统综合平台软件,该软件系统主要由终端工作站软件(FS-Client)和中心服务器软件(FS-Server)这两个部分组成,该平台上的基础算法库可以不断扩展,指纹处理流程也可以灵活配置。这个平台软件的应用范围也很灵活,既可作为研究过程中新算法的测试平台,也可配置为具体的应用系统,如指纹门禁考勤系统等。
本文内容主要包括:第一章概述了指纹识别的背景和研究意义,分析了国内和国际上指纹识别技术的研究现状、相关的典型算法和评估方式,并介绍了在本文中频繁出现的脉冲耦合神经网络模型及其应用情况;第二章在现有的PCNN模型基础上,针对一些新的图像处理应用特点,对PCNN的传统模型进行了改进,提出了一种基于PCNN的图像混合噪声去除方法,也对一种统一的高维PCNN进行了研究,并给出了该模型的一维、二维以及三维仿真结果,同时还对这种模型潜在的应用进行了展望;第三章提出了一种基于I-PCNN的滤波的指纹图像增强算法,并对该方法进行了仿真实验,给出了相关的测试结果数据;第四章提出了一种通过图像块主脊线和投影距离方差值来估算方向场的新颖方法,包括用PCNN来确定图像块主脊线、根据主脊线的投影距离方差来计算图块方向、以及方向场的后续校正,并给出了详细的实验结果;第五章研究了指纹图像的细化问题,提出了一个利用PCNN同步脉冲和方向约束的二值指纹图像细化算法,该方法在方向场的约束之下,先对图像进行粗细化后再进行精修剪;第六章提出了两种指纹模式分类方法,一种利用PCNN和LVQ,另一种主要通过多层树形SVMs来分类;第七章设计实现了一种算法库可以灵活扩展的指纹系统平台,该平台模块间接口稳定,能够增加新的算法模块:最后一章归纳总结本了本文的主要工作,并对生物特征识别技术领域的前沿热点问题和后续研究进行了展望。
Biometrics is a class of technologies that uses person's biologic features, such as face and voice, to verify a person, herein fingerprint is one of these widely adopted biologic features. Fingerprint recognition system often includes some important processing steps, such as fingerprint enhancement, orientation field computation, feature extraction, minutiae matching and pattern classification, etc. In recent years, the technologies and applications related to fingerprint recognition have obtained tremendous progress; nevertheless, there still exist many unsolved problems. These problems could make the application fields of fingerprint recognition system to face many restrictions. Many organizations are still addressing the research on novel technologies and applications for fingerprint, and they have achieved much valuable harvest, and the harvest could promote the application position of fingerprint in the person identification by biometrics.
In the beginning, this paper makes a brief review on the existing pulse coupled neural networks (PCNNs), and then, proposes several modified PCNN models, such as the weighted linking PCNN (WL-PCNN), the improved PCNN (I-PCNN) and template-based PCNN (TB-PCNN). On the basis of such models as these, it develops some new image processing algorithms, such as the mixed noise removal. Moreover, this paper researches on the high-dimensional models, as a result, it completes a universal model that is compatible with the traditional two-dimensional ones, then it exhibits some simulations of the pulse characteristics.
In addition to the above, this paper proposes some new processing algorithms for the important modules in fingerprint recognition system, such as fingerprint ridge enhancement, fingerprint orientation field computation, binary fingerprint image thinning and fingerprint pattern classification. On the basis of such algorithms as these, this paper designs an integrative fingerprint system, which consists of the client station software (named as FS-Client) and the central service software (named as FS-Server). In this system, the algorithm libraries can be extended, and the fingerprint processing flow can be customized flexibly. The system can be taken as a testing platform of algorithms, as well as a special application system by customization, for example, a door-controlling system.
As a whole, this paper mainly includes these parts: the first chapter gives out the research background and significance of fingerprint technologies, and discusses current research situation at home and overseas, the fingerprint algorithms and their evaluation rules, as well as the pulse coupled neural network that frequently appears in the following chapters. The second chapter makes some modifications for the existing PCNNs, and then develops several image processing algorithms using these modified models. The third chapter proposes a fingerprint enhancement algorithm using the modified model, and exhibits some experimental results of such an approach. The fourth develops a novel method for fingerprint orientation field computation, which determines the primary ridge by a simplified PCNN, then computes the local directions by projective distance variances, and makes a correction for the initial orientation field by a low-pass filtering. The fifth chapter proposes a coarse-to-fine thinning algorithm for binary fingerprint image. This method can restrict the thinning direction by orientation field, so it seldom generates pixel spurs. The sixth chapter proposes two fingerprint classification approaches. One is based on PCNN and learning vector quantization (LVQ) networks, and the other is based on a tree-like hierarchical SVM (support vector machine) classifier. The seventh one designs an integrative fingerprint system platform. In the last chapter, we summarize the work of this paper and give out some hot topics for the future research on biometric and fingerprint system.
本文首先对传统的脉冲耦合神经网络(PCNN,Pulse Coupled Neural Network)进行了研究,提出了几种改进的模型如“M-PCNN”、“I-PCNN”和“TB-PCNN”等,在这些模型基础之上,为PCNN开发了一些新图像处理应用,如混合噪声滤除。本文对PCNN的高维形态进行了初步研究,设计了一种能够兼容传统二维网络的高维统一模型,此外还对这种模型的网络结构、神经元的连接输入、活性调制、脉冲发生规则等进行了讨论,并对高维网络的脉冲特性进行了试验仿真。
以各种类型的PCNN为基础,为指纹识别系统中的关键处理模块提出了一些新的处理算法,主要包括“指纹脊线增强”、“指纹的方向场估计”、“二值指纹图像细化”和“指纹模式分类”,并在这些基本的处理算法基础之上,展示了在研究过程中开发的一套指纹系统综合平台软件,该软件系统主要由终端工作站软件(FS-Client)和中心服务器软件(FS-Server)这两个部分组成,该平台上的基础算法库可以不断扩展,指纹处理流程也可以灵活配置。这个平台软件的应用范围也很灵活,既可作为研究过程中新算法的测试平台,也可配置为具体的应用系统,如指纹门禁考勤系统等。
本文内容主要包括:第一章概述了指纹识别的背景和研究意义,分析了国内和国际上指纹识别技术的研究现状、相关的典型算法和评估方式,并介绍了在本文中频繁出现的脉冲耦合神经网络模型及其应用情况;第二章在现有的PCNN模型基础上,针对一些新的图像处理应用特点,对PCNN的传统模型进行了改进,提出了一种基于PCNN的图像混合噪声去除方法,也对一种统一的高维PCNN进行了研究,并给出了该模型的一维、二维以及三维仿真结果,同时还对这种模型潜在的应用进行了展望;第三章提出了一种基于I-PCNN的滤波的指纹图像增强算法,并对该方法进行了仿真实验,给出了相关的测试结果数据;第四章提出了一种通过图像块主脊线和投影距离方差值来估算方向场的新颖方法,包括用PCNN来确定图像块主脊线、根据主脊线的投影距离方差来计算图块方向、以及方向场的后续校正,并给出了详细的实验结果;第五章研究了指纹图像的细化问题,提出了一个利用PCNN同步脉冲和方向约束的二值指纹图像细化算法,该方法在方向场的约束之下,先对图像进行粗细化后再进行精修剪;第六章提出了两种指纹模式分类方法,一种利用PCNN和LVQ,另一种主要通过多层树形SVMs来分类;第七章设计实现了一种算法库可以灵活扩展的指纹系统平台,该平台模块间接口稳定,能够增加新的算法模块:最后一章归纳总结本了本文的主要工作,并对生物特征识别技术领域的前沿热点问题和后续研究进行了展望。
Biometrics is a class of technologies that uses person's biologic features, such as face and voice, to verify a person, herein fingerprint is one of these widely adopted biologic features. Fingerprint recognition system often includes some important processing steps, such as fingerprint enhancement, orientation field computation, feature extraction, minutiae matching and pattern classification, etc. In recent years, the technologies and applications related to fingerprint recognition have obtained tremendous progress; nevertheless, there still exist many unsolved problems. These problems could make the application fields of fingerprint recognition system to face many restrictions. Many organizations are still addressing the research on novel technologies and applications for fingerprint, and they have achieved much valuable harvest, and the harvest could promote the application position of fingerprint in the person identification by biometrics.
In the beginning, this paper makes a brief review on the existing pulse coupled neural networks (PCNNs), and then, proposes several modified PCNN models, such as the weighted linking PCNN (WL-PCNN), the improved PCNN (I-PCNN) and template-based PCNN (TB-PCNN). On the basis of such models as these, it develops some new image processing algorithms, such as the mixed noise removal. Moreover, this paper researches on the high-dimensional models, as a result, it completes a universal model that is compatible with the traditional two-dimensional ones, then it exhibits some simulations of the pulse characteristics.
In addition to the above, this paper proposes some new processing algorithms for the important modules in fingerprint recognition system, such as fingerprint ridge enhancement, fingerprint orientation field computation, binary fingerprint image thinning and fingerprint pattern classification. On the basis of such algorithms as these, this paper designs an integrative fingerprint system, which consists of the client station software (named as FS-Client) and the central service software (named as FS-Server). In this system, the algorithm libraries can be extended, and the fingerprint processing flow can be customized flexibly. The system can be taken as a testing platform of algorithms, as well as a special application system by customization, for example, a door-controlling system.
As a whole, this paper mainly includes these parts: the first chapter gives out the research background and significance of fingerprint technologies, and discusses current research situation at home and overseas, the fingerprint algorithms and their evaluation rules, as well as the pulse coupled neural network that frequently appears in the following chapters. The second chapter makes some modifications for the existing PCNNs, and then develops several image processing algorithms using these modified models. The third chapter proposes a fingerprint enhancement algorithm using the modified model, and exhibits some experimental results of such an approach. The fourth develops a novel method for fingerprint orientation field computation, which determines the primary ridge by a simplified PCNN, then computes the local directions by projective distance variances, and makes a correction for the initial orientation field by a low-pass filtering. The fifth chapter proposes a coarse-to-fine thinning algorithm for binary fingerprint image. This method can restrict the thinning direction by orientation field, so it seldom generates pixel spurs. The sixth chapter proposes two fingerprint classification approaches. One is based on PCNN and learning vector quantization (LVQ) networks, and the other is based on a tree-like hierarchical SVM (support vector machine) classifier. The seventh one designs an integrative fingerprint system platform. In the last chapter, we summarize the work of this paper and give out some hot topics for the future research on biometric and fingerprint system.
引文
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