虹膜辨識系統利用人類虹膜豐富的紋理特徵，來分辨出人的身份。虹膜影像某些區域常常因被眼皮、眼睫毛遮蓋住所造成的雜訊，在編碼時會被誤認成虹膜紋理的一部份而產生錯誤的虹膜資訊，進而直接影響之後的虹膜辨識結果，所以，正確去除這些雜訊是很重要的。
本論文主要提出一個有效且迅速的虹膜定位方法，選擇虹膜最不易受到干擾的可靠區域抽取特徵並利用極座標轉換做正規化與進一步的去除細部的錯誤區域，接著再利用高斯差旋積運算進行特徵編碼，以期達到高辨識率的目的。
實驗結果顯示，本文所提之虹膜辨識系統無論在時間效率或辨識率上都有令人滿意的結果。

Iris recognition system uses iris for distinguishing the identity of person using rich iris texture feature. To effectively remove noise and precisely segment stable iris region is a crucial stage prior to recognition. Most noises on iris images are caused by occlusion of eyelids or eyelashes in some certain areas. In this paper, we proposed an iris recognition system, which precisely locates and segments iris regions. We extract iris feature from a relatively reliable portion of the iris region using the DoG filter. Experimental results show that the proposed iris recognition system has satisfactory results in terms of time efficiency and recognition rate.

目錄
第一章 緒論 1
第二章 相關文獻回顧 4
第三章 虹膜辨識系統 8
3.1 虹膜內邊緣偵測 9
3.1-1 二值化（Binarization） 11
3.1-2 連通元件分析（Connected component） 11
3.1-3 斷開（Opening） 12
3.1-4 閉合（Closing） 14
3.1-5 投影法 15
3.2 虹膜外邊緣偵測 17
3.3 虹膜影像正規化 21
3.4 去除易受干擾區域 22
3.5 特徵碼擷取 25
3.5-1 長條圖等化（Histogram equalization） 25
3.5-2 高斯差（Difference of Gaussians） 25
3.6 特徵碼比對 26
第四章 實驗結果與討論 28
第五章 結論 31
參考文獻 32
附錄：英文論文 35

圖目錄
圖1. 虹膜於眼球中的結構圖 2
圖2. 虹膜豐富的紋理 2
圖3. Kang [9]提出的眼睫毛偵測法之結果圖 5
圖4. Mehrotra [13]提出的基於扇形正規化法 6
圖5. 正向視角與非正向視角的示意圖 7
圖6. 瞳孔偵測失敗影響之一例 9
圖7. 瞳孔與眼睫毛連結成同個物件的例子 10
圖8. 連通元件分析結果圖 12
圖9. 斷開運算的結果圖 13
圖10. 相機光源在瞳孔產生的反光，經過斷開後的結果 14
圖11. 閉合運算的結果 15
圖12. 上眼皮遮蓋瞳孔造成錯誤 16
圖13. 投影法去除雜訊 16
圖14. 有無使用投影法的瞳孔中心定位結果 17
圖15. 外邊緣的觀察 18
圖16. 濾波器示意圖 18
圖17. 濾波器的處理範圍 19
圖18. 濾波器對睫毛的反應示意圖 20
圖19. 虹膜的外圓定位結果 20
圖20. 極座標轉換法示意圖 21
圖21. 線性內插縮放例子 22
圖22. Mehrotra[13]提出的Sector-based normalization 23
圖23. 過大的遮蓋範圍造成無法去除的雜訊 23
圖24. 增加去除範圍後之比較圖 24
圖25. 正規化影像60*160 24
圖26. 長條圖等化結果 25
圖27. 高斯差之示意圖 26
圖28. 高斯差旋積運算結果 26

表目錄
表1. 資料庫CAISA V1.0的辨識結果，刪除不同區域的比較 29
表2. 資料庫CAISA V3.0的辨識結果，與Mehrotra[13]比較 29
表3. 資料庫CAISA V1.0與V3.0的辨識率 30

[1] L. Flom and A. Safir, “Iris recognition system,” U. S. Patent no. 4641349, 1987.
[2] J. Daugman, “High confidence visual recognition of persons by a test of statistical independence, ” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1148-1161, 1993.
[3] J. Daugman, “How iris recognition works,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 21-30, 2004.
[4] J. Daugman, “New methods in iris recognition,” IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 37, no. 5, pp. 1167-1175, 2007.
[5] R. P. Wildes, J. C. Asmuth, G. L. Green, S. C. Hsu, R. J. Kolczynski, J. R. Matey, and S. E. McBride, “A system for automated iris recognition,” in Proceedings of the Second IEEE Workshop on Applications of Computer Vision, pp. 121-128, 1994.
[6] R. P. Wildes, “Iris recognition: an emerging biometric technology,” in Proceedings of the IEEE, vol. 85, no. 9, pp. 1348-1363, 1997.
[7] D. R. S. Kumar, K. B. Raja, N. Nuthan, B. Sindhuja, P. Supriya, R. K. Chhotaray, and S. Pattnaik, “Iris recognition based on DWT and PCA,” in Proceedings of 2011 International Conference on Computational Intelligence and Communication Networks (CICN), pp. 489-493, 2011.
[8] Chia-Te Chou, Sheng-Wen Shih, Wen-Shiung Chen, Victor W. Cheng, and Duan-Yu Chen, “Non-orthogonal view iris recognition system,” IEEE Trans. Circuits and Systems for Video Technology, vol. 20, no. 3, pp. 417-430, 2010.
[9] Byung Jun Kang and Kang Ryoung Park, “A robust eyelash detection based on iris focus assessment,” Pattern Recognition Letters, vol. 28, no. 13, pp. 1630-1639, 2007.
[10] Zhaofeng He, Tieniu Tan, Zhenan Sun, and Xianchao Qiu, “Robust eyelid, eyelash and shadow localization for iris recognition,” in Proceedings of 15th IEEE International Conference on Image Processing, pp. 265-268, 2008.
[11] Tae-Hong Min and Rae-Hong Park “Comparison of eyelid and eyelash detection algorithms for performance improvement of iris recognition,” in Proceedings of 15th IEEE International Conference on Image Processing, pp. 257-260, 2008.
[12] Namrata P. Joshi, Roopal K. Lamba, Devang U. Shah, and Bhargav V. Ghadia, “Implementation of various approaches for iris image normalization,” in Proceedings of 2011 Nirma University International Conference on IEEE Engineering (NUiCONE), pp. 1-5, 2011.
[13] H. Mehrotra, G. S. Badrinath, B. Majhi, and P. Gupta, “An efficient iris recognition using local feature descriptor,” in Proceedings of 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 1957-1960, 2009.
[14] D. M. Monro, S. Rakshit, and D. Zhang, “DCT-based iris recognition,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 586-596, 2007.
[15] R. Chen, X. R. Lin, and T. H. Ding, “Iris segmentation for non-cooperative recognition system,” Image Processing, IET, vol. 5, no. 5, pp. 448-456, 2011.
[16] Chinese Academy of Sciences Institute of Automation, CASIA V1.0 Iris Image Database, http://biometrics.idealtest.org/