臺灣博碩士論文加值系統

在電腦視覺和圖形辨識領域的科學家及工程師們，正努力實現一套智慧型系統用於辨識生活中各種不同有用的資訊。在生物特徵中包括有指紋、虹膜、臉形、掌紋、聲紋、簽名等，由於指紋具有獨特性與不變性且基於成本等各種考量，使得指紋辨識在各種生物資訊中最廣為科學家及工程師們所研究、發展，將指紋辨識應用於有關安全性的系統上。指紋辨識中比對的方式有許多，但最廣為研究使用的為Minutiae-based，而利用Minutiae-based比對方式所需處理的動作，例如：影像增強、二值化、細線化等這些動作往往是很費時；特徵點數目的不同，特徵點的匹配和比對亦是需要不少的計算時間。指紋影像經細線化後容易產生誤判的特徵點，且比對的準確度往往受到指紋影像品質的影響。
另有學者提出不以特徵點做為指紋的比對特徵，而將指紋影像視為具方向性的紋理結構，利用指紋紋線資訊做為比對特徵數據。利用Gabor filterbank頻率與方向擷取出指紋影像中全域與區域資訊當作固定長度的特徵向量(Fingercode)，而比對的方式為計算兩指紋影像Fingercode的歐式距離，計算出它們的相似或相異程度，故其比對速度快。本論文提出改善Ridge Feature-based比對方式，處理有關指紋影像偏轉的問題，增加辨識準確性與正確性，以Nios-Stratix 發展板實現成硬體晶片，提供了一個指紋辨識發展平台，以實驗室購買的電容式指紋擷取器，擷取16位同學左拇指與右拇指的指紋影像，每隻拇指按壓兩次共64張指紋影像，整體辦識成功率為96.875%。

The scientists and engineers of the field in computer vision and pattern recognition, make great efforts to realize that a set of intelligent systems are being used for recognizable all kinds of useful information in life. The biological characteristics include fingerprint, iris, shape of face, palm, voiceprint, signing, etc. Because fingerprint has uniqueness and invariable and consideration of cost, fingerprint is most widely used for personal identification in security. Among fingerprint matching algorithms can be broadly classified as minutiae-based and ridge feature-based. The widely used minutiae-based needs to deal with image enhancement, binarization, thinning that are very time-consuming. It usually detects many of the false minutiae and quality of fingerprint is critical fact of accuracy.
A.K. Jain[20] thought fingerprint feature as oriented texture and proposed filter-based algorithm use a bank of Gabor filters to capture both local and global information in fingerprint as a compact fixed length Fingercode. The matching is to finding the Euclidean distance between these Fingercode, hence the matching is very fast.
This thesis proposes improving ridge feature-based deal with the problem about rotation and transfer of fingerprint, and increase the accuracy. I implement the fingerprint identification to the system on a programmable chip with Altera’s Nios Stratix development kit and use capacitive sensor to capture the fingerprint of left thumb and right thumb of 16 classmates. The accuracy of matching is 96.875%.

目錄

中文摘要 I
英文摘要 III
目錄 V
圖目錄 VIII

第一章 緒論 1
1.1前言 1
1.2指紋辨識原理和方法概述 2
1.3本文研究方向 4
第二章 指紋特徵資訊與相關背景 5
2.1指紋影像擷取裝置 5
2.2指紋影像中的失真與雜訊 7
2.3指紋影像的特徵 10
2.3.1指紋影像中的紋線與谷線 10
2.3.2指紋影像的宏觀辨識 11
2.3.3指紋影像的粗略分類 15
2.3.4指紋影像的細觀辨識 18
2.3.5擷取指紋特徵點 19
2.4指紋比對種類 21
2.5本文所採用比對方式 23

第三章 可規劃系統單晶片SoPC 24
3.1 SoPC系統設計流程 24
3.2 Nios辨識系統硬體模組設計流程 26
3.3 Nios辨識系統軟體設計流程 31
第四章 系統架構 32
4.1軟體設計流程 32
第五章 研究方法與理論 35
5.1指紋影像前處理 35
5.1.1正規化 36
5.1.2平滑處理與直方圖等化 38
5.2流向計算 39
5.2.1 Slit-sum 39
5.2.2由梯度方向來獲得指紋紋線方向 45
5.3擷取奇異點 54
5.3.1形態遮罩 54
5.3.2 Poincare Index 58
5.3.3 Sine Component Map 61
5.3.4各參考點搜尋法結果之比較 67
5.4指紋影像縮減 73
5.5扇形化 75
5.6濾波二值化 81
第六章 Fingercode特徵擷取與比對 91
第七章 模擬與實作結果 100
7.1辨識系統模擬於電容式指紋擷取器 101
7.2辨識系統模擬於光學式指紋擷取器 105
7.3辨識系統模擬於熱感應掃描式指紋擷取器 109
第八章 結論與未來研究方向 113
參考文獻 115

圖目錄

圖1.1指紋辨識系統架構 3
圖2.1光學感測式擷取器所擷取到的指紋影像 5
圖2.2電容感測式擷取器所擷取到的指紋影像 6
圖2.3原始影像與其平移狀況 7
圖2.4原始影像與其旋轉狀況 7
圖2.5原始影像與其擷取區域偏上方狀況 8
圖2.6原始影像與其部分影像重疊狀況 8
圖2.7原始影像與其按壓力道不均偏重或手指較溼的狀況 8
圖2.8原始影像與其按壓力道不均偏輕或手指較乾的狀況 9
圖2.9指紋影像中的痕溝 9
圖2.10指紋影像中之紋線與谷線 10
圖2.11指紋影像中的核心點與三角點 11
圖2.12弧形類 12
圖2.13帳形類 13
圖2.14右箕形紋 13
圖2.15左箕形紋 14
圖2.16螺旋形類 14
圖2.17雙箕螺紋 15
圖2.18粗略的分類演算流程 16
圖2.19指紋細微特徵類型 18
圖2.20特徵點 18
圖2.21典型的指紋特徵點擷取流程圖 19
圖3.1 SoPC系統設計流程 25
圖3.2 Nios辨識系統晶片架構圖 26
圖3.3 SoPC Builder元件模組設定 27
圖3.4 Nios的系統設定 28
圖3.5 Nios辨識系統模組產生 29
圖3.6 Quartus II的BDF硬體模組 30
圖3.7 Nios SDK Shell執行畫面 31
圖4.1本文指紋辨識系統主體架構 32
圖5.1前處理流程 35
圖5.2原始指紋影像與其直方圖 37
圖5.3正規化後的指紋影像與其直方圖 37
圖5.4平滑處理後的指紋影像與其直方圖 38
圖5.5直方圖等化後的指紋影像與其直方圖 38
圖5.6指紋紋路之八種方向 40
圖5.7 9x9之遮罩 40
圖5.8 Silt-sum流向計算之流程 40
圖5.9 WxW區塊 41
圖5.10選取區塊中最黑與最白像素點 41
圖5.11以9x9遮罩套入8x8區塊中最黑與最白之像素點位置 41
圖5.12原始指紋影像 43
圖5.13修正前的指紋流向圖 43
圖5.14修正方式 44
圖5.15修正後的指紋流向圖 45
圖5.16指紋影像中區域梯度方向與流向方向 45
圖5.17 3x3 Sobel運算子遮罩 46
圖5.18黃色區塊流向 47
圖5.19梯度向量場中對立方向的向量 47
圖5.20原始指紋影像 49
圖5.21指紋流向圖 49
圖5.22平均修正 49
圖5.23手指按壓力道不均 50
圖5.24指紋影像品質差 51
圖5.25指紋影像品質不佳 52
圖5.26 核心點位置 54
圖5.27三角點位置 55
圖5.28利用形態遮罩擷取奇異點流程 56
圖5.29核心點附近之流向 56
圖5.30三角點附近之流向 56
圖5.31奇異點位置 57
圖5.32計算Poincare Index流程 58
圖5.33核心點位置 59
圖5.34三角點位置 59
圖5.35各指紋類型利用Poincare Index所擷取到奇異點位置 60
圖5.36 Poincare Index於不同指紋影像品質的表現 60
圖5.37弧形類 61
圖5.38 Concave ridges 61
圖5.39原始指紋影像與平滑處理後的指紋流向場 62
圖5.40 Since component map 62
圖5.41上半圓遮罩 63
圖5.42參考點位置與相對應e(i,j)之位置 64
圖5.43弧形類與其Sine component map 65
圖5.44非弧形類之指紋影像 67
圖5.45各參考點搜尋法結果之比較 68
圖5.46各參考點搜尋法結果於指紋影像品質不佳下之比較 69
圖5.47未經低通濾波所得到指紋流向圖 71
圖5.48低通濾波後所得到指紋流向圖 71
圖5.49未經低通濾波，Poincare index所擷取到可能的參考點位置 72
圖5.50經經低通濾波，Poincare index所擷取到可能的參考點位置 72
圖5.51指紋影像中參考點可能位置 74
圖5.52扇形化流程圖 75
圖5.53扇形化各參數 76
圖5.54圓盤區域經扇形化後劃分為64個扇形區塊 77
圖5.55定義指紋影像座標軸與四個象限位置 79
圖5.56扇形區塊編碼 80
圖5.57定義Gabor空間遮罩參數 82
圖5.58 Gabor各旋轉角度 82
圖5.59八個方向的Gabor空間遮罩 83
圖5.60指紋影像增強 84
圖5.61指紋影像增強與流向正確性之關係 85
圖5.62不同指紋影像品質經影像增強後的結果 86
圖5.63手指按壓力道不均經影像增強後的結果 87
圖5.64八個方向的近似Gabor空間遮罩 88
圖5.65 Gabor空間遮罩與近似Gabor空間遮罩效果之比較 89
圖6.1正規化前後之指紋影像 91
圖6.2指紋Fingercode特徵擷取流程 92
圖6.3濾波後影像 93
圖6.4正規化與影像增強效果之比較 94
圖6.5原始指紋影像與其Fingercode 96
圖6.6一般手指按壓時無偏轉的情況 97
圖6.7參考點下方區域之流向與旋轉修正 98
圖6.8 Tented Arch類與其參考點下方區域之流向 99
圖7.1 Kuo之指紋影像 102
圖7.2 Hao之指紋影像 103
圖7.3 Min與Wei之指紋影像 104
圖7.4 FVC2002 DB2資料庫中的指紋影像 105
圖7.5 7_6、7_7、7_8之參考點與其扇形化和正規化後的指紋影像 106
圖7.6 7_6、7_7、7_8各指紋影像之Fingercode 107
圖7.7 FVC2004 DB3資料庫中的指紋影像 109
圖7.8 89_7、96_8參考點誤判 110
圖7.9指紋影像參考點附近痕溝影響參考點擷取的準確性 111
圖7.10 FVC2004 DB3資料庫中指紋影像比對成功的例子 112
圖8.1指紋流向場與特徵點和指紋紋線數 114

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