臺灣博碩士論文加值系統

近年來用生物特徵做為辨識已廣泛應用在個人身份辨識上，而虹膜識別是生物辨識技術中最準確的辨識方法之一; 早在2004年，德國法蘭克福機場就開始應用虹膜辨識系統，引進身份辨識裝置，藉由虹膜掃描辨識，連結到護照資料的資料庫，進行身份比對；而近年來虹膜辨識越來越廣泛的應用在個人身份辨識上，就連手機也開始應用虹膜辨識系統，可見生物辨識的重要性越來越受到重視。

傳統虹膜辨識技術主要是將虹膜特徵區域利用極座標方式轉換為方形矩陣，接著將方形矩陣做特徵碼轉換，最後利用特徵碼去匹配；而本論文與傳統虹膜辨識系統不同的地方在於，為了避免眼瞼以及睫毛干擾，所以本論文之虹膜區域分別只擷取靠近瞳孔周圍之環形區域以及下半圓之虹膜區域做為對照比較；另一個與傳統辨識不同的地方為特徵匹配部分，傳統特徵匹配利用特徵碼去做匹配，而本論文是採圖像特徵的方式去做匹配，採用的方法為尺度不變特徵轉換SIFT(Scale-invariant feature transform)；SIFT是採用圖像的局部特徵，對旋轉尺度縮放亮度變化保持不變性，對視角變化仿射變換、雜訊也保持一定程度的穩定性，因此用SIFT做為虹膜的特徵匹配是非常適合的。

本論文之虹膜辨識準確率為95％，跟其他採用相同資料庫且也採用SIFT做為匹配方法的論文相比較，辨識效能近似。

Biometrics has been applied to the personal recognition popularly and it becomes more important. The iris recognition is one of the biometric identification methods, and the technology can provide the accurate personal recognition. As early as 2004, the German airport in Frankfurt began to use the iris identification system. By the iris scan identification, the iris information is linked to the passport data database, and the personal identity is functional. In recent years, the iris identification is used widely and increasingly in personal identifications. Even the mobile phone also begin to use the iris identification system, and the importance of biometrics gains more and more attention.

The traditional iris recognition technology mainly transforms the iris feature region into a square matrix by using the polar coordinate method, and the square matrix is transformed to the feature codes, and then the signature is used to the feature match finally. The difference between the proposed and the traditional iris recognition systems is : to avoid the eyelid and eyelash interferences, the retrieved iris region in the proposed design only locates near the pupil around the ring area and the lower half of the iris area for recognitions. On the other side, the traditional iris identification uses the feature code matching technology; however, the proposed method uses the image feature matching technology, i.e. the scale-invariant feature transform (SIFT) method. The SIFT uses the local features of the image, and it keeps the feature invariance for the changes of rotation, scaling, and brightness. The SIFT also maintains a certain degree of stability for the change of the perspective affine transformation and noises. Therefore, it is very suitable that the SIFT technology is applied to iris feature matching.

In the proposed design, the accuracy of the iris recognition is 95%. Compared with other methods by using the same database and the similar SIFT technology as the matching method, the recognition performance of the proposed design is suitable.

誌謝 i
論文摘要 ii
Abstract iii
目錄......................................................iv
圗目錄…………………………………………………………………v
表目錄…………………………………………vii
第一章 緒論 1
1.1研究動機與目的 1
1.2系統架構 2
1.3論文架構 3
第二章 預備知識與相關研究 4
2.1預備知識 4
2.1.1高斯濾波(Gaussian blur) 4
2.1.2 Canny邊緣偵測 4
2.1.3隨機抽樣一致性(RANSAC,Random Sample Consensus)[11] 5
2.1.4直方圖等化(Histogram Equalization) 5
2.2相關研究 6
2.2.1 虹膜影像資料庫[14] 6
第三章 演算法流程 9
3.1瞳孔定位 12
3.1.1積分運算子 12
3.1.2霍夫圓變換[19] 13
3.1.3邊緣偵測及霍夫轉換 13
3.2擷取虹膜區預 14
3.3特徵加強 15
3.3.1限制對比度自適應直方圖等化(CLAHE) 15
3.3.2 賈柏濾波(Gabor filter)[8] 17
3.4特徵匹配 18
3.4.1尺度不變特徵轉換(SIFT) [6] 18
3.4.2 隨機抽樣一致性(RANSAC) 19
第四章 實驗流程與實驗結果 20
4.1實驗 20
4.1.1實驗一 20
4.1.2實驗二 29
4.2實驗結果與分析 36
實驗環境 36
錯誤接受率、錯誤拒絕率與等錯誤率 36
匹配結果 37
第五章 結論及未來需改進地方 38
參考文獻 39

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[3] 霍夫圓變換,"http://www.jianshu.com/p/68868cfc3409"
[4] 限制對比度自適應直方圖等化法原理、實現及效果,
"http://www.cnblogs.com/Imageshop/archive/2013/04/07/3006334.html "
[5] 直方圖等化(Histogram equalization),
"http://honglung.pixnet.net/blog/post/83681254-image-processing---%e7%9b%b4%e6%96%b9%e5%9c%96%e7%ad%89%e5%8c%96%28histogram-equalization%29"
[6] SIFT特徵提取算法,
"https://read01.com/zh-tw/66k3KP.html#.WZ5M39IjG70 "
[7] 眼睛的結構,"http://pei.cjjh.tc.edu.tw/~pei/light/instrument_explain.htm"
[8] Gabor filter學習,
"http://blog.csdn.net/jinshengtao/article/details/17797641"
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[13] Rafael C.Gonzalez,Richard E. Woods, "Design Image Processing"
[14] Chinese Academy of Sciences Institute of Automation (CASIA) Iris Database, http://biometrics.idealtest.org/findTotalDbByMode.do?mode=Iris
[15] John G.Daugman, “High confidence recognition of persons by iris patterns,” IEEE 35th International Carnahan conference on Security Technology,254~263,2001.
[16] Hematian, A.; Manaf, A.A; Chuprat, S.; Khaleghparast, R. ;Yazdani, S., "Field Programmable gate array system for real-time IRIS recognition", 2012 IEEE Conference on Open systems(ICOS).

[17] Zhaofeng He,Tieniu Tan, Zhenan Sun, Xianchao Qiu, "Toward Accurate and Fast Iris Segmentation for Iris Biometrics," IEEE Trans.Patt.Anal. and Machine Intell., Vol.31, No. 9. September 2009.
[18] Kazuyuki Miyazawa, Koichi Ito, Takafumi Aoki , Koji Kobayashi, Hiroshi
Nakajima, "An Effective Approach for Iris Recognition Using Phase-Based Image
Matching" IEEE Trans.Patt.Anal.and Machine Intell., Vol.30,No.10,October 2008.
[19] DUDA,Richard O.; HART,Peter E. User of the Hough transformation to detect lines and curves in pictures . Communications of the ACM,1972, 15.1:11-15.