指紋辨識系統是現今最廣泛使用之生物辨識技術之一，且現今已經被廣泛應用在使用者安全認證及電子商務上。在指紋比對技術中，又以眾所周知minutiae為基礎之特徵比對方法最被廣泛使用。其中在許多以minutiae為基礎之指紋辨識系統中以「端點及叉點」為最顯著的兩個minutiae特徵。

一般而言，指紋辨識系統普遍包含以下處理之程序：「指紋影像截取、指紋影像前處理、指紋影像細線化、指紋影像後處理、指紋特徵點抽取及指紋特徵點比對等」; 然而，在實際上指紋辨識系統的比對辨識效能經常受參差不齊品質特徵點所影響。當假的minutiae特徵被萃取出來，然合法的minutiae特徵被忽略遺忘，甚至合法之特徵點卻記載著錯誤特徵點資訊時，綜觀以上之現象，將使得在比對過程中造成不良且錯誤結果的產生。

因此，當指紋影像中實際萃取出粗劣品質特徵點時，對指紋辨識系統而言，將是構成不良的正確辨識效能因素之一。所以，對於如何去搜尋一個較佳關係代表相對應的指紋特徵(minutiae)或是在往後處理程序中（如指紋比對程序）能有個依據將針對品質粗劣之特徵提供一個選擇之方法，試著以量化方式去描述特徵點品質是相當重要的。

在本論文中，我們在萃取特徵品質中探討並針對其特徵品質作定義，並且根據定義提出詳細的演算流程去測量特徵品質。此特徵品質測量方法，主要是建構在指紋影像處理過程中對於特徵點周遭結構變化之特性，以及方向領域的一致性上，去給予相對應的特徵一個品質分數。在論文中，主要目的是利用上述方法並透過量化方式去獲得特徵品質數值。透過量化方式去表達特徵品質數值使得我們能清楚、明確地指出特徵的品質及穩地性; 在安全性方面應用，我們在以minutiae為基礎的指紋比對技術中，期望特過整合原有之特徵點資訊(座標、型態及方向)及我們所量化之特徵品質數值，使得在比對程序當中能有個依據針對優良及劣等的特徵點品質有不同的處理方式，並使得指紋比對效能能更進一步的提升。

目前我們已經進行多項的實驗，由實驗結果可證明經過整合特徵品質因素後之指紋比對技術相較於未整合之指紋比對技術確實能夠減少錯誤比對率。

Fingerprint recognition system is one of the more popular
and reliable applications used security access and E-commerce. Minutiae-based fingerprint matching is the most well-known and widely used method. Most two prominent features of minutia are ridge ending and ridge bifurcation in many minutiae-based fingerprint recognition system. General, fingerprint recognition system consists of fingerprint image acquisition, preprocessing, thinning, postprocessing, fingerprint feature extraction, and
fingerprint matching. However, the identification performance of fingerprint recognition system on the different quality of extraction of minutiae is very sensitive. Spurious minutiae being produced, valid minutiae being lost, and the feature of minutia
being wrongly labeled are resulted in caused the undesirable result. Hence, poor quality of the actual minutiae feature of a fingerprint image constitutes the single most cause of poor accuracy performance of a fingerprint recognition system. Therefore, it is important to quantify the quality of minutiae for either seeking a better representation of the minutia or for subjecting the poor minutia to alternative methods of processing (e.g., matching).

In this thesis, we explore a definition of quality of extracted minutiae and present detailed algorithms to measure minutiae quality. The proposed quality measure has been developed with the use of fingerprint image processing properties and the alignment of orientation field. The goal of the thesis is to obtain the value of the minutia quality quantified by above method. Based on value of
the minutia quality, we can express clear its stability. For security, we combine information of the minutiae quality with the minutiae-based fingerprint matching techniques existed to make the matching more powerful.

In experiments, it is shown that the matching error rate is reduced compared with uncombined the minutiae quality method, and their performance is evaluated using a FVC2002 dataset.

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