指纹识别中遗传算法的应用及硬件实现
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摘要
随着信息科学技术的发展，利用人体生物特征进行身份鉴别技术的研究，已成为世界上许多国家的研究热点。指纹是人体的生物特征之一，利用指纹进行身份鉴别的技术在一些发达国家已得到广泛的应用。近几年，指纹识别技术的研究及其应用在我国掀起了一个热潮，对于指纹图像的处理和识别，各种指纹识别解决方案都有其不同的侧重点，本论文的研究工作侧重点是追求识别的准确性和识别设备的便携性，以便于刑侦等特殊场合的应用。鉴于此，本论文的研究目的在于提出高准确度的指纹识别算法，并在硬件上加以实现。目前，在指纹识别算法的研究方面，国内外指纹识别算法基本上都是采用基于细节点特征的，从研究角度来说，国内外的差距并不明显。但是，国外一些发达国家由于起步较早，研究手段和设备比较先进，因此在这方面的研究已经比较成熟，自动指纹识别系统在这些国家的很多领域已经得到广泛应用。相对来说，国内在指纹识别算法研究方面起步比较晚，大约是从八十年代开始，而且主要侧重于研究角度，很长一段时间没有在实际应用中实践，所以这些技术和实际的市场需求之间还有不少差距。目前自动指纹识别技术的研究方向主要集中在：预处理、特征提取和指纹图象分类、匹配算法的研究、指纹图像的压缩存储、开发用于实时特征提取和匹配的专用电路。
本论文的研究工作主要包括指纹图像的预处理、特征提取和特征匹配的算法研究，并在此基础上利用硬件实现了一个自动指纹识别系统。
本论文的研究目标：研究遗传算法在指纹识别技术中的应用，并在此基础上进行自动指纹识别系统的硬件实现。本论文的侧重点是要达到较高的匹配准确性，以及识别设备的便捷性，以便适合于一些特殊的应用，如刑侦，电子门锁，以及一些要求准确率和保密性较高的身份识别场合。
研究工作的突出点表现在如下三个方面：第一，对采集到的指纹图像进行了有效的预处理，便于提取指纹的特征点及以后的匹配工作。第二，提出一种基于遗传算法的指纹识别算法，使指纹试别的准确性和效
率都达到了相当的程度。第二，对自动指纹识别系统进行硬件的设计与制作，以DSP为核心芯片，结合CPLD技术，在实现自主指纹识别算法的基础上构造了其硬件平台。
全文分为五章，第一章是绪论，介绍指纹识别技术的基本知识及其发展历史与国内外现状。第二章介绍自动指纹识别技术的一般组成和各个环节的功能及处理方法。第三章详细介绍识别算法中具体实现方法，本论文是以点匹配的方式式线指纹的识识别。由采集器采集来的裸格式指纹不能直接用来提取特征点，所以，为了较好的得到指纹的特征点，我们需要对指纹图像进行一定的预处理。
指纹的预处理分为几个步骤，首先就是对指纹图像进行去噪处理：由采集器采集到的指纹图像会不同程度地受到各种噪声地干扰，另外，由于指纹图像是通过模模拟数字转换后以矩阵的形式存入计算机中的，在数字化和量化的过程中必然会引入噪声。通常这些噪声由于其中时间上是不相干的，因而含有较高的空间频谱。因此需要在分类或特征提取之前对采集到的指纹图像进行去噪处理。平滑滤波的任务就是去除这些干扰噪声，使图像的失真尽可能的少。平滑滤波在一定程度上减少了指纹采集所带来的噪声对后续处理产生的影响。第二步是对平滑处理后的图像进行锐化处理：由于获取指纹图像的条件限制，可能会造成指纹脊线和谷线之间的灰度变化不明显或不均匀，同时也由于在平滑处理中，会使指纹纹线边缘模糊，这是由于高的空间频率成分比低的空间频率成分弱这一原因造成的。因而为了强化指纹纹线间的界限，突出边缘信息，增强高的空间频率成分，以利于二值化，锐化处理是必要的，锐化处理对于增强反差和检测边缘是很有用的。虽然经过平滑处理,在一定程度上去除了指纹图像中的随机噪声,但是总的来说, 图像的对比度还是相当地弱。指纹图锐化是为了增强纹线与背景的对比度，使纹线轮廓更清楚，从而使指纹的分割更容易进行。第三步是对锐化处理后的图像进行二值化处理：在完成图像增强和去噪以后，就可以进行脊线突出了。有的文献提出直接去灰度图像中提取特征点，其理由是这样可以避免在细化过程中可能出现的伪特征点，以便充分利用指纹图象中原有的灰度信息。但更多的研究者是在提取指纹骨架的基础上获取特征点。尽管脊线在原始的灰度图像中存在着浓度的变化，但其真正的可用之处只是简单
的二值：脊线（灰度值为0）和背景（灰度值为1）。二值化就是将输入的灰度图像转化成二进制表示的图像，在它们上面不呈现出灰度的变化，这样做有利于后续处理工作。对于指纹的识别来说，有用的信息是包含在脊线和谷线的二值描述中。因而必须根据原始的灰度图像来确定图像上的每一点应属于前景区域还是背景区域，从而产生对应的二值图像。它不仅可以大大减少存储量，而且使得后的判别过程少受干扰，大大简化其后的处理方法。二值图像是后续处理的基础，它的算法对后续处理有直接的影响，一个好的算法可以得到一个高质量的二值图像。第四步是对二值图像进行细化处理，以提取骨架：经过二值化处理的指纹图象，尚有一定的宽度，为便于提取特征点，一般采用细?
With the development of information science and technology, the recognition and identification of persons by use of physiological characteristics have become one of the research focuses in computer science and pattern recognition in many countries all over the world. Fingerprint, one of the most stable human physiological characteristics, has been widely used for personal identity recognition in developed countries. In recent years, researches on fingerprint recognition techniques and their applications also raised upsurges in our country. Unfortunately, most of them are focused rather on the secondary development of the techniques purchased from abroad than on the study of self-owned algorithms and their hardware realization. Considering this fact, we will concentrate ourselves on the development of self-owned algorithms and their hardware realization. This thesis aims at the development and presentation of such a fingerprint system. From the research point of view, the difference between them is not clear. But, some developed countries have done a farther research because of an early start, advanced, research methods and equipments. Automatic recognition system has already been widely used in many fields in these countries. Comparatively speaking, our research in this area started relatively late, from around 80S. It emphasized the research angle, so that it was hot used in practice, So，there is long distance to make these techniques meet the market’s need. Recently, the research of automatic fingerprint recognition concentrates on preprocessing, characteristics acquisition, classification of fingerprint images, researches in correspondent algorithm, compression and storage of the fingerprint images, exploitation for real time characteristics extraction and special circuit for matching.
The research presented in the thesis mainly includes algorithm research on pre-processing, feature extraction and feature matching of fingerprint images, and the hardware realization of an automatic fingerprint identification system based on the algorithm we developed.
The highlights of our research work are the following: (1) The pretreatment to the image to make the process that is going to be done easier. (2) Application of “ genetic algorithm ”strategy to fingerprint image matching, allowing to be more accurate and feasible, introduced by relative translation and rotation between different images of a same fingerprint. A successful feature matching is thus achieved. (3) Design and implementation of hardware realization of the fingerprint identification system. On the basis of our self-owned algorithms, a hardware platform
with DSP as core chip, combined with PLD technique, has been constructed.
There are 5 chapters in all.
Chapter1: Overview, an introduction to the basic knowledge of the technology, the history of its development and the status inward and abroad.
Chapter2: an introduction to the general composition, the function of each. Chapter3: A detailed explanation of the specific political method
In this thesis, the focus is on the mode of to realize the fingerprint recognition, The original fingerprint directly from sensor can net be used to gain the characteristic point, So we need to preprocess the fingerprint image in order to get better characteristic.
The pre-processing is composed of several steps. First of all, the removement of noise from the fingerprint image is to be done. The reason is that the image will be interfered by various noises. In addition, the fingerprint images are stored in the computer in form of matrix after A/D、D/A transformation, so the noise is unavoidable in the process of digitalization and quantification .Usually, these noises are unrelated to time, so they contain high-level space frequency register. All of those show us the explaination of the necessity of removing the noise before characteristic extraction. The task of smoothing is to get rid of jamming noise and minimize image distortion. To some degree, smoothing reduces the influence caused by the noise on the f
本论文的研究工作主要包括指纹图像的预处理、特征提取和特征匹配的算法研究，并在此基础上利用硬件实现了一个自动指纹识别系统。
本论文的研究目标：研究遗传算法在指纹识别技术中的应用，并在此基础上进行自动指纹识别系统的硬件实现。本论文的侧重点是要达到较高的匹配准确性，以及识别设备的便捷性，以便适合于一些特殊的应用，如刑侦，电子门锁，以及一些要求准确率和保密性较高的身份识别场合。
研究工作的突出点表现在如下三个方面：第一，对采集到的指纹图像进行了有效的预处理，便于提取指纹的特征点及以后的匹配工作。第二，提出一种基于遗传算法的指纹识别算法，使指纹试别的准确性和效
率都达到了相当的程度。第二，对自动指纹识别系统进行硬件的设计与制作，以DSP为核心芯片，结合CPLD技术，在实现自主指纹识别算法的基础上构造了其硬件平台。
全文分为五章，第一章是绪论，介绍指纹识别技术的基本知识及其发展历史与国内外现状。第二章介绍自动指纹识别技术的一般组成和各个环节的功能及处理方法。第三章详细介绍识别算法中具体实现方法，本论文是以点匹配的方式式线指纹的识识别。由采集器采集来的裸格式指纹不能直接用来提取特征点，所以，为了较好的得到指纹的特征点，我们需要对指纹图像进行一定的预处理。
指纹的预处理分为几个步骤，首先就是对指纹图像进行去噪处理：由采集器采集到的指纹图像会不同程度地受到各种噪声地干扰，另外，由于指纹图像是通过模模拟数字转换后以矩阵的形式存入计算机中的，在数字化和量化的过程中必然会引入噪声。通常这些噪声由于其中时间上是不相干的，因而含有较高的空间频谱。因此需要在分类或特征提取之前对采集到的指纹图像进行去噪处理。平滑滤波的任务就是去除这些干扰噪声，使图像的失真尽可能的少。平滑滤波在一定程度上减少了指纹采集所带来的噪声对后续处理产生的影响。第二步是对平滑处理后的图像进行锐化处理：由于获取指纹图像的条件限制，可能会造成指纹脊线和谷线之间的灰度变化不明显或不均匀，同时也由于在平滑处理中，会使指纹纹线边缘模糊，这是由于高的空间频率成分比低的空间频率成分弱这一原因造成的。因而为了强化指纹纹线间的界限，突出边缘信息，增强高的空间频率成分，以利于二值化，锐化处理是必要的，锐化处理对于增强反差和检测边缘是很有用的。虽然经过平滑处理,在一定程度上去除了指纹图像中的随机噪声,但是总的来说, 图像的对比度还是相当地弱。指纹图锐化是为了增强纹线与背景的对比度，使纹线轮廓更清楚，从而使指纹的分割更容易进行。第三步是对锐化处理后的图像进行二值化处理：在完成图像增强和去噪以后，就可以进行脊线突出了。有的文献提出直接去灰度图像中提取特征点，其理由是这样可以避免在细化过程中可能出现的伪特征点，以便充分利用指纹图象中原有的灰度信息。但更多的研究者是在提取指纹骨架的基础上获取特征点。尽管脊线在原始的灰度图像中存在着浓度的变化，但其真正的可用之处只是简单
的二值：脊线（灰度值为0）和背景（灰度值为1）。二值化就是将输入的灰度图像转化成二进制表示的图像，在它们上面不呈现出灰度的变化，这样做有利于后续处理工作。对于指纹的识别来说，有用的信息是包含在脊线和谷线的二值描述中。因而必须根据原始的灰度图像来确定图像上的每一点应属于前景区域还是背景区域，从而产生对应的二值图像。它不仅可以大大减少存储量，而且使得后的判别过程少受干扰，大大简化其后的处理方法。二值图像是后续处理的基础，它的算法对后续处理有直接的影响，一个好的算法可以得到一个高质量的二值图像。第四步是对二值图像进行细化处理，以提取骨架：经过二值化处理的指纹图象，尚有一定的宽度，为便于提取特征点，一般采用细?
With the development of information science and technology, the recognition and identification of persons by use of physiological characteristics have become one of the research focuses in computer science and pattern recognition in many countries all over the world. Fingerprint, one of the most stable human physiological characteristics, has been widely used for personal identity recognition in developed countries. In recent years, researches on fingerprint recognition techniques and their applications also raised upsurges in our country. Unfortunately, most of them are focused rather on the secondary development of the techniques purchased from abroad than on the study of self-owned algorithms and their hardware realization. Considering this fact, we will concentrate ourselves on the development of self-owned algorithms and their hardware realization. This thesis aims at the development and presentation of such a fingerprint system. From the research point of view, the difference between them is not clear. But, some developed countries have done a farther research because of an early start, advanced, research methods and equipments. Automatic recognition system has already been widely used in many fields in these countries. Comparatively speaking, our research in this area started relatively late, from around 80S. It emphasized the research angle, so that it was hot used in practice, So，there is long distance to make these techniques meet the market’s need. Recently, the research of automatic fingerprint recognition concentrates on preprocessing, characteristics acquisition, classification of fingerprint images, researches in correspondent algorithm, compression and storage of the fingerprint images, exploitation for real time characteristics extraction and special circuit for matching.
The research presented in the thesis mainly includes algorithm research on pre-processing, feature extraction and feature matching of fingerprint images, and the hardware realization of an automatic fingerprint identification system based on the algorithm we developed.
The highlights of our research work are the following: (1) The pretreatment to the image to make the process that is going to be done easier. (2) Application of “ genetic algorithm ”strategy to fingerprint image matching, allowing to be more accurate and feasible, introduced by relative translation and rotation between different images of a same fingerprint. A successful feature matching is thus achieved. (3) Design and implementation of hardware realization of the fingerprint identification system. On the basis of our self-owned algorithms, a hardware platform
with DSP as core chip, combined with PLD technique, has been constructed.
There are 5 chapters in all.
Chapter1: Overview, an introduction to the basic knowledge of the technology, the history of its development and the status inward and abroad.
Chapter2: an introduction to the general composition, the function of each. Chapter3: A detailed explanation of the specific political method
In this thesis, the focus is on the mode of to realize the fingerprint recognition, The original fingerprint directly from sensor can net be used to gain the characteristic point, So we need to preprocess the fingerprint image in order to get better characteristic.
The pre-processing is composed of several steps. First of all, the removement of noise from the fingerprint image is to be done. The reason is that the image will be interfered by various noises. In addition, the fingerprint images are stored in the computer in form of matrix after A/D、D/A transformation, so the noise is unavoidable in the process of digitalization and quantification .Usually, these noises are unrelated to time, so they contain high-level space frequency register. All of those show us the explaination of the necessity of removing the noise before characteristic extraction. The task of smoothing is to get rid of jamming noise and minimize image distortion. To some degree, smoothing reduces the influence caused by the noise on the f
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