| 论文首页哲学论文经济论文法学论文教育论文文学论文历史论文理学论文工学论文医学论文管理论文艺术论文 |
摘 要
指纹具有唯1性和稳定性，因此被人们用来当作鉴别个人身份的主要依据。自动指纹识别系统是基于计算机来进行指纹识别的技术，具有方便、高效、安全、可靠等优点，在金融安全、数据加密、电子商务等各个领域都得到了广泛的应用，并将在我们的生产和生活中发挥越来越重要的作用。
本文的内容正是关于自动指纹识别系统的研究，按照设计过程，指纹识别主要包括3个大部分:指纹图像的预处理、特征提取以及匹配。
指纹图像的预处理又可以分为灰度图滤波去噪、2值化、2值化图像去噪、细化和细化后去噪5个部分。本文先基于指纹的方向图设计出方向滤波器对原图像进行滤波去噪，然后使用局部平滑阈值自适应2值化算法，将灰度图像进行2值化，并采用快速傅氏变换对所得到的2值化图像进行去噪处理。接下来使用细化模板对2值化图像进行细化，并针对细化图中各种噪声的拓扑结构将它们11滤除。
指纹图像的特征提取主要是提取指纹的细节特征及其位置。本文先采用脊线跟踪法将指纹图中的细节特征全部找出来，再对每个细节特征进行验证，尽量去除伪特征点。然后采用求Poincare Index值的方法确定指纹的中心点，并作为参照点来确定每个特征点相对参照点的位置。
指纹图像的匹配过程包括了图像校准和细节匹配两个部分。首先，找到输入图像和模板图像的参照点对，然后将两幅图像中的细节特征点相对于各自的参照点转化为极坐标形式，最后进行比对，确定两幅图像是否来自于同1手指。
关键词：预处理；特征提取；匹配；2值化；细化；细节特征
Abstract
Due to the uniqueness and persistence, fingerprint is used as main basis of personal identity. Automated fingerprint identification system is a technology of fingerprint identification by computer, which is of convenience, high efficiency, security and reliability. It has been applied in many fields such as financial security, data encryption, electronical business and some, and will play a more and more important role in our life. 您可以访问中国科教评价网（www.NsEac.com）查看更多相关的文章。
The paper is about the study of automated fingerprint identification system. According to the process of the design, the paper can be devided into three components: pre-processing, feature extraction, matching of fingerprint images.
Fingerprint image pre-processing has five parts: filtration in gray-scale image, binarization, filtration in binary image, thinning and filtration in thinning image. In the paper, we firstly design orientation filters based on directional image of fingerprint and employ them to denoise gray-scale image. Then, we binarize the gray-scale image with local self-adaptive binarization smoothness algorithm andeliminate the noises from the binary image with fast Fourier transform algorithm.Afterwards, by using thinning templates, we get the skeleton fingerprint imagefrom the binary image. After thinning, we get rid of the noises from the acquired skeleton image according to their configuration.
Fingerprint image feature extraction mainly extracts the minutiae and their positions. Firstly, this paper presents an algorithm based on ridge following to extract all minutiae from the pre-processed image. Secondly, we validate these minutiae and eliminate pseudo ones. Then, by computing the value of Poincare Index, we can find the core of the fingerprint. Finally, we can fix on the relative positions of the minutiae according to the core.
Fingerprint image matching has two steps: image adjustment and minutiae matching. First of all, We select a referrence point pair of the input image and the template image. And then we transform the minutiae positions into polar coordinates. Finally, we match the input image with the template one to judge whether these two images are captured from the same finger.
Keywords: pre-processing; feature extraction; matching; binarization; thinning; minutiae