生物特徵是人們與生俱來的，包含指紋、掌紋、聲紋、人臉、虹膜與視網膜等訊息，其具有不易仿冒與獨一無二的特性。它的應用範圍含括了身份辨識、犯罪偵查、命理占卜、加密上鎖、身份登入以及門禁控管。隨著科技產業的蓬勃發展，資訊系統需要更佳縝密的授權控管機制，才能避免重要資料的外洩。因此，身份辨識的功能成為設計現今及未來資訊系統中不可或缺的環節。在日常生活中，經常使用的鑰匙、晶片卡或RFID感應卡等身份認證機制，皆存在遺失與被盜錄的風險。而指紋是目前各國政府出入境部門與企業或個人門禁控管中使用最廣泛的生物特徵之一。因此，吾人希望能藉由區域二元特徵（Local Binary Pattern），萃取指紋之紋理特性，做統計之分析與評量，設計出一套可靠與完善的指紋辨識系統，以加強個人資料之控管。
本論文透過平台掃描器與手機相機兩種輸入裝置取得指紋影像。平台掃描器的取像策略為利用印泥將三種不同按壓力道（輕按、中按及重按）的指紋拓印在普通白紙上，再利用Epson Perfection V33掃描機掃描影像；手機相機則使用感光元件為Sony Exmor RS™ for mobile的Sony Xperia ZL C6502行動電話來拍攝指紋影像。影像經由二值化、雜訊處理與邊界選取等影像處理之方法做前置處理，並利用不同範圍的區域二元特徵來萃取其紋理特性。系統在Intel Core 2 Duo P8700時脈2.53GHz的個人電腦與Windows 7之作業系統的環境下，吾人針對實驗室11位同學進行實驗測試，兩種輸入方式的系統正確辨識率分別為100% 與100%。而透過Cross Match、Digital Persona及AuthenTec三間公司提供的四組標準指紋資料庫，其中AuthenTec有兩組，人數分別為51、65、21與16人，本系統之辨識率可分別達到85.62%、78.46%、95.24%及100.0%。

Biometric features are innate characteristics of human beings, including fingerprint, palm-print, voice-print, face, iris and retina. They are unique, difficult to counterfeit, and widely used in the fields of person identification, crime investigation, fortune telling, data encryption, computer login and facility access. With the rapid development of technologies, more delicate authorization mechanism is required to build reliable information systems and avoid potential confidentiality disclosure. Therefore, user identification capability is indispensable for designing modern and future information systems. In our daily life, keys, IC cards and RFID cards are usually accompanied by the risk of missing and embezzling. Fingerprint is one of the most widely used biometric features for the immigration offices, business facilities and personal properties around the world. By the use of local binary pattern, it is our sincere hope to design a trustworthy fingerprint recognition system to enhance data integrity.
In this thesis, two input devices, a scanner and a mobile camera, are used to acquire the fingerprint images. For the scanner scenario, three levels of weights with light, moderate and heavy pressures are applied to rub the inked fingerprints on the paper, and then the Epson Perfection V33 scanner is utilized to capture the images. For the mobile camera approach, the Sony Xperia ZL C6502 mobile phone with Sony Exmor RS™ camera is used to obtain the fingerprint images. Image binarization, noise reduction and boundary selection are applied in the preprocessing steps, and local binary pattern is then adopted to extract the texture features. Under the Intel Core 2 Duo P8700 @ 2.53GHz personal computer and Windows7 operating system environment, correct rates of 100% and 100% can be obtained using the scanner and mobile camera schemes respectively for a database with 11 laboratory users. In addition, the system is tested on the Cross Match Technology database of 51 users, Digital Persona database of 65 users and AuthenTec databases of 21 users and 16 users, the correct fingerprint recognition rates of 85.62%, 78.46%, 95.24% and 100.0% can be achieved respectively.

論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
目錄 v
圖目錄 viii
表目錄 xi
第1章 緒論 1
1-1研究動機 1
1-2 研究目的與動機 2
1-3 研究方法與步驟 4
1-4 論文章節概要 5
第2章 文獻探討 6
2-1指紋歷史的演進 6
2-2指紋的構造與分類 8
2-3 指紋辨識 12
2-3.1 影像辨識法 12
2-3.2 紋路辨識法 12
2-3.3 特徵點辨識法 13
2-4 指紋特徵點 14
2-4.1 全域辨識法 15
2-4.2 區域辨識法 17
第3章 指紋辨識系統架構 19
3-1 指紋辨識流程 20
3-2 影像前置處理 21
3-2-1 影像二值化 21
3-2-2 雜訊處理 26
3-2-3 邊界選取 29
3-2-4 指紋影像對比強化 31
3-3 指紋特徵萃取 37
3-3-1 區域二元特徵Local Binary Pattern 37
第4章 辨識系統訓練策略及效益 40
4-1 硬體設備與系統參數 40
4-2 指紋資料庫建立與訓練 41
4-2-1 印泥拓印資料庫建立與訓練 41
4-2-2 手機相機資料庫建立與訓練 43
4-3權重分析 45
4-4 系統辨識效能 46
4-4-1 印泥拓印 46
4-4-2 手機相機拍攝 47
4-3-3 資料庫測試 47
第5章 結論與未來展望52
參考文獻 53

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