指紋識別算法研究

指紋識別算法研究

畢業論文

摘 要

指紋具有唯1性和穩定性，因此被人們用來當作鑒別個人身份的主要依據。自動指紋識別系統是基于計算機來進行指紋識別的技術，具有方便、高效、安全、可靠等優點，在金融安全、數據加密、電子商務等各個領域都得到了廣泛的應用，并將在我們的生產和生活中發揮越來越重要的作用。
    本文的內容正是關于自動指紋識別系統的研究，按照設計過程，指紋識別主要包括3個大部分:指紋圖像的預處理、特征提取以及匹配。
指紋圖像的預處理又可以分為灰度圖濾波去噪、2值化、2值化圖像去噪、細化和細化后去噪5個部分。本文先基于指紋的方向圖設計出方向濾波器對原圖像進行濾波去噪，然后使用局部平滑閾值自適應2值化算法，將灰度圖像進行2值化，并采用快速傅氏變換對所得到的2值化圖像進行去噪處理。接下來使用細化模板對2值化圖像進行細化，并針對細化圖中各種噪聲的拓撲結構將它們11濾除。
指紋圖像的特征提取主要是提取指紋的細節特征及其位置。本文先采用脊線跟蹤法將指紋圖中的細節特征全部找出來，再對每個細節特征進行驗證，盡量去除偽特征點。然后采用求Poincare Index值的方法確定指紋的中心點，并作為參照點來確定每個特征點相對參照點的位置。
指紋圖像的匹配過程包括了圖像校準和細節匹配兩個部分。首先，找到輸入圖像和模板圖像的參照點對，然后將兩幅圖像中的細節特征點相對于各自的參照點轉化為極坐標形式，最后進行比對，確定兩幅圖像是否來自于同1手指。
關鍵詞：預處理；特征提��；匹配；2值化；細化；細節特征

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.
    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

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