臺灣博碩士論文加值系統

人終其一生，人眼虹膜結構除非眼睛病變否則不會產生任何變化。這是它的優點也是它的缺點，優點：虹膜穩定度高，運用在辨識上有很好的效果。缺點：一旦被竊取仿造會造成莫大的損失。本論文將會深入研究序列虹膜影像進行辨識與防偽之基本原理。系統架構，主要包含五個模組：影像擷取、活體偵測、影像前處理、特徵萃取與分類辨識模組。首先，擷取設備取得連續人眼影像，再經由活體偵測模組利用影像處理演算法，將取出序列人眼影像的資訊，來判斷連續虹膜影像的真偽。經由前處理模組利用影像處理演算法，自人眼影片中取得虹膜部份。再利用虹膜影像邊緣偵測與反射光源面積之差異，來估測影像品質因素，並利用所估測的分數取得一張最好的虹膜影像進行辨識。然後使用特徵萃取模組，利用拉氏金字塔轉換（Laplacian Pyramid Transform）與特徵眼（Eigeniris）萃取代表身分的特徵碼。最後，分類辨識模組，則利用特徵碼進行辨識或註冊的動作。
以實驗室自建的資料庫，總共有25個志願者的虹膜影片，共175個影片，以及偽造虹膜影片，共175個影片。對系統做活體偵測進行測試，以等錯率（Equal Error Rate, EER）為標準，辨偽率可達96.57%。甚至在要求安全性較高的系統中錯誤接收率（False Acceptance Rate, FAR）設定為0%的情況下，辨識成功率（Acceptance of Authentic, AA）可達92.43%。本論文會針對實驗的數據結果進行分析比較，來驗證建構本系統時所提的相關理論，以供後續研究作為參考。

Through the whole human life, the structure of iris does not change unless the eyes get diseased. That is its advantage as well as disadvantage. Advantage: the Iris is highly stable and can result in very good recognition. Disadvantage: there will be a great loss if it is stolen or counterfeited. We will study deeply the methods for recognition and anti-forgery from iris image sequence. The system architecture includes five modules: image acquisition, detection liveness, image pre-processing, feature extraction and recognition modules. Firstly, our system captures a sequence of iris images of a human eye. The detection liveness module adopts coefficients from the input iris sequences to determine if the eyes are live or just replay the fake iris images. The preprocessing module uses some image processing algorithms to extract the region of interest of iris from the sequence of human eye images, it can use differences resulted from the iris image edge detection and the area of light reflection to estimate the iris image quality. Then, the extraction module uses Laplacian pyramid transformation and eigeniris to extract the characteristic codes for identity. Finally, the system applies these characteristic codes for iris matching in recognition module.
We have designed experiments for some assumed interesting scenarios, and launched them on our video database which consists of 175 iris image sequences of 25 classes, and 175 fake iris image sequences. The proposed mechanism can successfully detect the forgery attacks. The differentiation forgery rate of the system for video input data can achieve up to EER=96.57%. Even under the circumstance of false acceptance rate （FAR）0%, the system still approaches the differentiation rate in 92.43%. This thesis analyzes the experiment results and verity the proposed methods for further research.

論文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
符號說明 ix
第一章 緒論 1
1.1 研究動機 1
1.2 虹膜辨識技術之發展與研究 3
1.3 研究目標與方向 7
1.4 論文大綱與組織 8
第二章 人眼虹膜所需要之影像處理技術 9
2.1 影像強化 9
2.1.1 基本灰階轉換 9
2.1.2 空間域濾波 11
2.2 形態學影像處理 13
2.3 圓與曲線偵測 16
2.3.1 圓偵測 16
2.3.2 曲線偵測 18
第三章 序列虹膜影像活體偵測及影像品質估測與選取 19
3.1 偽造攻擊情節介紹 19
3.2 活體偵測 21
3.2.1 虹膜定位 21
3.2.2 活體辨識方法 27
3.3 影像品質估計與選取 29
3.3.1 影像邊緣估計影像品質 29
3.3.2 反射光源面積估測影像品質 31
3.3.3 影像選取 33
第四章 具有抗偽造之序列虹膜辨識系統 34
4.1 系統架構 34
4.2 影像擷取模組 35
4.3 活體偵測模組 36
4.4 影像前處理模組 36
4.4.1 虹膜定位 36
4.4.2 虹膜分割與正規化 38
4.4.3 影像品質估測與選取 40
4.4.4 影像強化 40
4.5 特徵萃取模組 41
4.5.1 拉氏金字塔轉換 42
4.5.2 特徵眼 43
4.5.3 特徵萃取-主成分轉換 45
4.6 分類辨識模組 48
第五章 具有防偽之虹膜辨識系統實作與實驗結果 50
5.1 實驗環境 50
5.2 系統效能的評估 52
5.3 拉氏金字塔轉換 53
5.4 偽造虹膜欺騙系統 55
5.5 活體辨識 56
5.6 實驗結果與討論 57
第六章 結論與建議 58
6.1 結論 58
6.2 後續研究方向 59
參考文獻 60

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