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研究生:袁上元
研究生(外文):Shang-Yuan Yuan
論文名稱:建構於碎形維之自動人眼虹膜辨識系統
論文名稱(外文):Automatic Iris Recognition System based on Fractal Dimension
指導教授:陳文雄陳文雄引用關係
指導教授(外文):Wen-Shiung Chen
學位類別:碩士
校院名稱:逢甲大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2000
畢業學年度:88
語文別:中文
論文頁數:233
中文關鍵詞:生物辨識系統人眼虹膜影像碎形幾何碎形維電腦視覺影像分析樣式識別類神經網路
外文關鍵詞:Biometric Recognition SystemIrisFractal GeometryFractalsChaosFractal DimensionImage AnalysisPattern Recognition
相關次數:
  • 被引用被引用:17
  • 點閱點閱:503
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
長久以來,生物辨識系統(Biometric Recognition System)之應用一直在生活中與我們習習相關,如:門禁管制系統、自動提款機、資料存取安全系統、身份辨識系統等等,皆是屬於生物辨識系統之範圍。良好的生物辨識系統除準確度要高且要不易作假外,對於使用者的接受度亦為評估之重點。近年來一些研究發現人眼眼球中之視網膜(retina)與虹膜(iris)皆具有獨特的特徵,尤其是每一個人的虹膜更是獨一無二,兩人相同的機率甚至較指紋來得低且極不易作假,同時人眼虹膜影像擷取的接受度又較視網膜影像擷取來得高。
由於每個人的虹膜都是相當仔細且獨特的紋路,並不因時間而改變,虹膜作為辨識的特徵應該具有極高的應用價值。1993年,John Daugman首先提出利用虹膜來作為生物辨識的特徵,採用Gabor轉換並結合類神經網路發展出第一套虹膜辨識系統。1994年,Wildes等人亦作了相關的研究。除此二套系統之外,已無其它文獻探討。有鑑於此,本論文將深入研究虹膜特徵,並設計一套自動虹膜辨識系統(Automatic Iris Recognition System),稱為--- “AIRS”。
碎形維(fractal dimension)是碎形理論中相當重要的性質,非常適合用來描述複雜的紋理(texture)特徵。由於虹膜影像中存在相當複雜的紋理組織且具有相當程度的碎形自我相似性,因此我們相信利用碎形維來描述人眼虹膜的特徵應該非常適當。然而碎形維的估計卻是一個相當困難的問題。在被發展出來的估計方法中,方盒計數(box-counting, BC)法是最普及的方法之一,經常用來估計二值影像的碎形維。後來,差值方盒計數(differential box-counting, DBC)法被設計來估計2-D灰階影像的碎形維。但是,方盒計數法與差值方盒計數法皆存在一些問題,它們經常會過度估計(over-estimate)方盒的數目使得所計算得到的碎形維並不精確。如何更精確地估計一個自然影像的碎形維成為極重要的問題。
本論文第一部份將深入探討這個問題,並提出三個計算法則(estimate algorithms)以期能更精確地估計一個2-D自然影像與1-D生理醫信號的碎形維。第一個方法是提出一個稱為移動差值方盒計數(shifting differential box-counting, SDBC)計算法則以改善差值方盒計數法。論文中也將由理論上證明此法會比舊法求得更精確的碎形維估計值。第二個方法是提出一個稱為掃描方盒計數(scanning box-counting, SBC)法之新計算法則。第三個方法是提出一個新方法用來估計2-D自然影像之相關維。這些方法應用於1-D的情況也將於論文中加以討論。論文中將以2-D自然紋理影像與1-D生理醫學信號如心電圖波形(ECG)與脈波(pulse)來對新計算法則加以驗證其效果。
本論文第二部份將深入研究人眼虹膜特徵,並設計一套自動人眼虹膜辨識系統,稱為--- “AIRS”。虹膜辨識系統整體的基本架構可分為前處理和樣式識別前後兩大單元:前處理(preprocessing)部份主要是負責將欲辨識之對象經由一些處理步驟找出具代表性的碼(code)給其後的樣式識別單元作處理,即此單元的輸入為欲辨識之對象而輸出為其特徵碼(feature code)。相對地,樣式識別(pattern recognition)部份主要是負責將具代表欲辨識對象的碼輸入分類比對模組(model)進行分類和比對處理以達到辨識分類之目的。前處理單元包含有虹膜影像擷取、影像前處理、影像強化和特徵抽取四大步驟其中影像前處理包含有虹膜影像之切割處理和瞳孔縮放之正規化,影像強化包含有高通濾波器和虹膜高頻影像之影像強化,而虹膜影像之特徵抽取則包含有虹膜影像特徵之切割準則和虹膜影像之特徵抽取。此篇論文主要是利用上述新型碎形維演算法取得虹膜影像的粗糙度作為特徵向量,再配合k-means法與類神經網路技術組成自動人眼虹膜辨識系統。
Biometrics and biometric recognition system become very important and valuable in the applications such as identification and security. Fingerprint identification has been the most widespread of application of biometric technology. Recently, iris identification is emerging as the most foolproof method of automated personal identification in demand by an ever more automated world. There are only two such iris recognition systems developed. In this thesis, a prototype system, called Automatic Iris Recognition System (AIRS), which is based on fractal dimension as feature description will be developed.
Fractal dimension is a fascinating feature highly correlated with the human perception of surface roughness and has been successfully applied to texture analysis, segmentation, and classification, In addition to theoretical Hausdorff dimension, box dimension and correlation dimension are two significant alternative definitions of fractal dimension, which are computationally manageable. The box-counting (BC) method and the differential box-counting (DBC) method are two popular methods in computing the fractal dimension for digital textured image. They, however, inhere in some drawbacks. In this thesis, three algorithms that can obtain more accurate estimate of the fractal dimension are proposed and investigated. First, a modified algorithm of the DBC method, is called the shifting DBC (SDBC) algorithm is proposed to improve the DBC method. We will theoretically prove that the SDBC algorithm approaches the estimated value closer to the exact fractal dimension than the DBC method. Second, a novel approach, called the scanning BC (SBC) algorithm, is introduced. Third, a novel approach to estimate the correlation dimension for 2-D natural image will be proposed and discussed. These algorithms used for 1-D case will also be investigated. Simulations on 2-D natural textural images and 1-D biomedical waveform sequences, such as ECG and pulse waves, will be performed and discussed.
Based on the fractal dimension obtained by the three algorithms as the feature description of iris image, an iris identification technique will be introduced and investigated. Finally, the prototype system (AIRS) of the automatic iris recognition system will developed. The simulation results are also given.
摘要 ............................................... i
Abstract .............................................. v
誌謝 .............................................. viii
圖目錄 .............................................. xiii
表目錄 .............................................. xxi
縮寫及符號 ..................................... xxii
第一章 緒論 ..................................... 1
1.1 前言 ..................................... 1
1.2 生物辨識系統之價值 ............................ 2
1.3 人眼虹膜辨識系統之重要性 ................... 4
1.4 論文研究之動機 ............................ 5
1.5 研究之目標 ............................ 9
1.6 論文之大綱與組織 ............................ 9
第二章 生物辨識技術之回顧 ............................ 11
2.1 基本的辨識系統架構 ............................ 11
2.2 以生物特徵為基礎之生物辨識技術 .......... 12
2.3 幾種重要之生物辨識技術 .................... 14
2.4 常用之型樣識別方法 ............................ 31
2.5 辨識系統之評估法則 ............................ 39
第三章 人眼虹膜辨識技術之回顧 ................... 42
3.1 概述 ...................................... 42
3.2 人眼虹膜影像 ............................. 42
3.3 以Gabor轉換為基礎之Daugman虹膜辨識系統 ...... 45
3.4 Wildes虹膜辨識系統 ............................ 53
3.5 研究問題 ...................................... 60
第四章 碎形理論與碎形維之回顧 .................... 62
4.1 前言 ...................................... 62
4.2 碎形與碎形幾何 ............................. 64
4.3 自我相似性 ............................. 65
4.4 碎形維 ...................................... 70
4.5 碎形維之幾個定義 ............................. 76
4.6 容積維 ...................................... 91
4.7 相關維 ...................................... 102
4.8 研究問題 ...................................... 104
第五章 三個新碎形維估計演算法則 .................... 106
5.1 方盒計數法之問題 ............................. 106
5.2 移動式方盒計數(SDBC)演算法 ........... 109
5.3 移動式方盒計數(SDBC)演算法之實驗結果 .. 116
5.4 掃描式方盒計數(SBC)演算法 ................... 118
5.5 掃描式方盒計數(SBC)演算法之實驗結果 .. 127
5.6 新型相關維估計演算法 .................... 132
5.7 新型相關維估計演算法之實驗結果 ........... 137
5.8 1-D信號之碎形維估計演算法 .................... 144
5.9 1-D碎形維估計於時間序列訊號之實驗結果與討論 .. 157
第六章 以碎形維理論為基礎之自動虹膜辨識系統¾AIRS .. 161
6.1 概述 ....................................... 161
6.2 以碎形維為特徵之自動虹膜辨識系統 ........... 162
6.3 AIRS系統之整體架構 .................... 164
6.4 虹膜影像擷取模組 ............................. 166
6.5 虹膜影像前處理模組 .................... 170
6.6 虹膜影像之碎形維特徵萃取模組 ........... 191
6.7 型樣識別模組 ............................. 194
第七章 AIRS系統實作與實驗結果 .................... 197
第八章 結論 ....................................... 214
參考文獻 ................................................ 217
附錄A 應用於影像壓縮之快速完全搜尋向量量化 ........... 226
附錄B 生理醫學ECG與Pulse波形訊號之資料壓縮 ........... 227
作者簡介 ................................................ 232
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/*----------------- Biometric Recognition Technology ------------------*/
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/*------------- Iridology & Iris Recognition Systems -------------*/
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/*----------------- Neural Networks ------------------*/
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