臺灣博碩士論文加值系統

生物辨識系統（Biometric Recognition System），利用人體獨一無二的生理特徵，作為密碼或通行證的方式，可省卻隨身攜帶鑰匙的麻煩，或者降低遺忘密碼的風險。它除了可應用在高安全性的出入口管制、自動提款機…等方面，亦可使用於新興的電子商務身分辨識。人類滿周歲後，虹膜不會因年齡或其他因素受到改變，又具有極高的唯一性，兩個人虹膜相同的機率遠比指紋相同的機率低得多﹔同時，人眼虹膜影像擷取的接受度亦較視網膜影像來得高，因此以虹膜作為辨識的特徵應具有極高的應用價值。
Karhunen-Loeve（KL）轉換現今已被廣泛的使用在信號表示、壓縮或分類上，而信號可經由KL轉換產生互不相關（uncorrelated）的特徵，去除掉信號的冗餘性（redundancy）。至於碎形維（fractal dimension）是碎形理論中相當重要的性質，它非常適合用來描述複雜的紋理（texture）。本論文即利用KL轉換和碎形維度兩數學模型來描述虹膜複雜的紋理組織，分別抽取出261 bytes和84 bytes的特徵向量，再配合k-means演算法，組成自動人眼辨識系統。

Traditionally, the methods for personal authentication are based on what a person possesses (e.g., key, ID card, etc.) or what a person knows (e.g., secret password, PIN number, etc.). However, these methods usually have the following problems. For example: keys may be falsified, ID cards may be lost, and passwords and PIN numbers may be forgotten. Accurate and reliable personal authentication (or identity authentication) is becoming more and more urgent to the operation of cybernetic society. In recent years, iris feature of a human eye is receiving growing interests due to its inherence of high uniqueness, high permanence, and high circumvention. An iris may provide a solution by offering a much more discriminating biometric than others such as fingerprint or face recognition.
Fractal dimension is relatively insensitive to an image scaling, and shows a strong correlation with human judgment of surface roughness. A commonly used method for determining the fractal dimension estimate of a fractal curve in the plane is the divider dimension. KL transform generates mutually uncorrelated and orthogonal features in order to avoid information redundancies.
In this thesis, an automatic personal authentication technique using a human iris image is presented. Two approaches to extract iris feature are proposed. First, we apply the divider dimension estimate algorithm in 1-D wavelet transform domain to analyze the texture of a human iris and extract its unique feature codes. Second, we employ the KL transform to extract the local features of an iris image. Finally, the experimental results are given.

摘要…………………………………………………………………….…..i
Abstract…………………………………………………………….………ii
誌謝………………………………………………………………….……iii
圖目錄………………………………………………………………..…..vii
表目錄……………………………………………………………...…….xii
第一章 緒論………………………………………………………..……..1
1.1生物辨識系統之價值…………………………………..…….1
1.2論文研究之動機………………………………………..…….3
1.3論文大綱………………………………………………..…….5
第二章 生物辨識技術之回顧……………………………………………7
2.1基本辨識系統架構…………………………………………...7
2.2以生物特徵為基礎之生物辨識技術………………………...7
2.3幾種重要生物辨識技術……………………………………...9
2.4 K平均（K-means）演算法………………………..………..24
2.5辨識系統之評估法則………………………………………25
第三章 人眼虹膜辨識技術之回顧………………………………….….28
3.1 前言………………………………………………………...28
3.2 以Gabor wavelet轉換為基礎之Daugman虹膜辨識系統.29
3.3 Wildes虹膜辨識系統……………………………………….36
3.4 Zhu、Tan與Wang之虹膜辨識系統…………………….….41
第四章 碎形、凌波轉換與KL轉換理論之回顧………..………………46
4.1碎形………………………………………………………….46
4.2凌波轉換…………………………………………………….68
4.3 Karhunen-Loeve轉換……………………………………….73
第五章 以圓規維與KL轉換為基礎之自動人眼虹膜辨識系統………79
5.1概述………………………………………………………….79
5.2以圓規維為特徵之虹膜辨識系統………………………….80
5.3以Karhunen-Loeve轉換（KLT）為特徵之虹膜辨識系統….81
5.4 AIRS系統之整體架構……………………………………..82
5.5虹膜影像擷取模組………………………………………….83
5.6虹膜影像前處理模組……………………………………….87
5.7虹膜影像之圓規維特徵萃取模組………………………….95
5.8虹膜影像之KL轉換特徵萃取模組………………………...98
5.9型樣識別模組……………………………………………...102
第六章 AIRS系統實作與實驗結果……………………………….….103
6.1圓規維演算法……………………………………………..105
6.2 KL轉換演算法……………………………………………107
6.3系統性能…………………………………………………..108
第七章 結論……………………………………………………………111
參考文獻…………………………………………………………….….113

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