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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/8412


    Title: 利用指紋紋路分佈順序及分佈模型作指紋自動分類;Fingerprint CLassification by Ridge DIstribution Sequences and Ridge Distribution Model
    Authors: 張正弘;Jeng-Horng Chang
    Contributors: 資訊工程研究所
    Keywords: 指紋分佈模型;指紋分佈順序;指紋分類;ridge distribution sequences;fingerprint classification;ridge distribution model
    Date: 2001-07-04
    Issue Date: 2009-09-22 11:26:18 (UTC+8)
    Publisher: 國立中央大學圖書館
    Abstract: 指紋自動辨識系統是以指紋紋路的端點及分岔點所構成的特徵群當做辨識的依據。傳統的特徵抽取法需要對指紋影像做二值化或是細線化這些前處理的步驟,不但需要較多的處理時間,更會產生紋路的斷裂或是紋路不當的交互連結。基於這些理由,我們提出直接在灰階影像下抽取特徵點的新方法,並且以此方法為基礎,發展出一個指紋分類系統的原創模型。 在灰階影像下抽取特徵點的第一個步驟便是要定出指紋影像中的紋路點。這個方法以我們所提出的一個多模灰階濃度分佈圖的分解技術為基礎,首先定出屬於紋路、溝紋及背景三個部分的灰階值範圍,並分析出代表此三個物件的分佈圖的參數。透過分析紋路的灰階值結構,利用統計方法,除了定出真正的指紋點外,並同時將處於影像背景中及位於同一條紋路中的多餘指紋點去除。實驗的結果證實所提出的方法能有效的找出完整指紋影像中96%以上的指紋點。利用橫向及縱向交錯的紋路抽取線定出所有的紋路點以後,以紋路點之間的平均灰階值決定紋路走向,最後便可以找出所有紋路的路線及其分支和端點等特徵點。 我們接下來提出了一個以指紋紋路的分佈順序為基礎的指紋分類系統。本方法不以區域性的特徵來決定分類,而是以指紋的全域特徵為考量的基礎。觀察各類型的指紋,我們發現組成所有指紋的紋路只有十種基本的樣式,而不同類型的指紋,都只是由這十種不同的指紋紋路樣式依照特定的順序所組合而成的。若所得出的紋路分佈順序能夠符合其中某一個已知類型的紋路排列順序,則接受這個指紋並指定其類型。指紋的紋路也是直接在灰階影像下進行分離,以減少前處理程序所耗去的處理時間。對於紋路的斷裂及分支,我們也提出了解決的辦法。這種方法能夠分出完整的七個Henry的分類,這一點是目前沒有其他方法能夠達成的。另外,我們也嘗試利用紋路分佈所具備的特質來定義出一個清楚的拒絕分類的標準。 Ridges and ravines are the main components constituting a fingerprint. Traditional Automatic Fingerprint Identification Systems (AFIS) are mainly based on minutiae matching techniques. The minutiae for fingerprint identification are defined by ridge terminations and ridge bifurcations. Most AFIS perform ridge line following process to automatically detect minutiae based on binary or skeleton fingerprint images. For low-quality fingerprint images, the preprocessing stage of an AFIS produces redundant minutiae or even destroys real minutiae. The minutiae detection algorithms in direct gray-scale domain have been developed to overcome these problems. The first step of gray-scale minutiae detection algorithm is to determine ridge locations and then perform gray-scale ridge line following algorithm to extract minutiae. However, the existing gray-scale minutiae detection techniques can only work on partial fingerprint images due to the ignorance of image background. Moreover, the gray value variation inside a ridge also generates redundant ridge points. In this dissertation, we propose a novel method, based on gray-level histogram decomposition, to locate the ridge points in complete fingerprint images. By decomposing the gray-level histogram, redundant ridge points can be eliminated according to some statistical parameters. Experimental results demonstrate that the correct rate can be over 96% even applied to poor-quality fingerprint images. For automatic fingerprint classification problem, a novel method is introduced which is a combination of structural and syntactic approaches. The goal of the proposed Ridge Distribution (R-D) Model is to present the idea of the possibility for classifying a fingerprint into the complete seven classes in the Henry's classification. From our observation, there exist only ten basic ridge patterns which construct fingerprints. Fingerprint classes can be interpreted as a combination of these ten ridge patterns with different ridge distribution sequences. In this thesis, the classification task is performed depending on the global distribution of the ten basic ridge patterns by analyzing the ridge shapes and the sequence of ridges distribution. The regular expression for each class is formulated and a NFA model is constructed accordingly. An explicit rejection criterion is also defined in this thesis. For the seven-class fingerprint classification problem, our method can achieve the classification accuracy of 93.4% with 5.1% rejection rate. For the five-class problem, the accuracy rate of 94.8% is achieved. Experimental results reveal the feasibility and validity of the proposed approach in fingerprint classification.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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