Please use this identifier to cite or link to this item:
標題: 以碎形維度為基礎的虹膜辨識系統
An Iris Recognition System Based on Fractal Analysis Using Entropy-Box-Counting Method
作者: 王世杰
Wang, Shih-Chieh
關鍵字: Iris recognition;虹膜辨識;Fractal dimension;Box counting;碎形維度
出版社: 資訊科學系所
引用: [1] L. Flom, A. Safir, Iris recognition system, US Patent No. 4641394, 1987. [2] J.G. Daugman, High confidential visual recognition by test of statistical independence, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 15, No. 11, pp. 1148-1161, 1993. [3] J.G. Daugman, The importance of being random: statistical principles of iris recognition, Pattern Recognition, Vol. 36, pp. 279-291, 2003. [4] R. Wildes, J. Asmuth, G. Green, S. Hsu, R. Kolczynski, J. Matey, S. McBride, A machine-vision system for iris recognition , Machine Vision and Application, Vol. 9, pp. 1-8, 1996. [5] W. Boles, B. Boashash, A human identification technique using images of the iris and wavelet transform, IEEE Trans. Signal Process., Vol. 46, No. 4, pp. 1185-1188, 1998. [6] S. Lim, K. Lee, O. Byeon, T. Kim, Efficient iris recognition through improvement of feature vector and classifier, ETRI J., Vol. 23, No. 2, pp. 61-70, 2001. [7] L. Ma, Y. Wang, T. Tan, Iris recognition based on multichannel Gabor filtering , Proceedings of ACCV 2002, Vol. I, pp. 279-283, 2002. [8] L. Ma, Y. Wang, T. Tan, Iris recognition using circular symmetric filters , 16th International Conference on Pattern Recognition, Vol. II, pp. 414-417, 2002. [9] L. Ma, T. Tan, Y. Wang, D. Zhang, Personal identification based on iris texture analysis , IEEE Trans. Pattern Anal. Mach. Intell., Vol. 25, No. 12, pp. 1519-1533, 2003. [10] L. Ma, T. Tan, Y. Wang, D. Zhang, Local intensity variation analysis for iris recognition , Pattern Recognition, Vol. 37, pp. 1287-1298, 2004. [11] L. Ma, T. Tan, Y. Wang, D. Zhang, Efficient iris recognition by characterizing key local variations , IEEE Trans. Image Process., Vol. 13, No. 6, pp. 739-749, 2004. [12] Li Yu, D. Zhang, K. Wang, The relative distance of key point based iris recognition, Pattern Recognition, Vol. 40, pp. 423-430, 2007. [13] B. Mandelbrot, Fractal geometry of nature, San Francisco: Freeman, 1982. [14] A. Pentland, Fractal-based description of natural scenes, IEEE Trans. Pattern Anal. Mach. Intell., Vol. PAMI-6, pp. 666-674, 1984. [15] J.M. Keller, S. Chen, R.M. Crownover, Texture description and segmentation through fractal geometry, Comput. Vision Graph Image Process., Vol. 45, pp. 150-166, 1989. [16] N. Sarkar and B. B. Chaudhuri, An efficient differential box-counting approach to compute fractal dimension of image, IEEE Trans. on Systems, Man. and Cybernetics, Vol. 24, No. 1, pp. 115-120, Jan. 1994. [17] CASIA Iris Image Database. ( [18] J. Canny, A computational approach to edge detection, IEEE Trans. Pattern Anal. Machine Intell., Vol. PAMI-8, pp. 679-698, Nov. 1986. [19] D. Ballard, Generalized Hough transform to detect arbitrary patterns, IEEE Trans. Pattern Anal. Machine Intell., Vol. PAMI-13, pp. 111-122, 1981. [20] Gonzalez, and Woods, Digital Image Processing 2nd Edition, Prentice Hall, 2002. [21] S. Prabhakar, S. Pankanti, A.K. Jain, Biometrics recognition: security and privacy concerns, IEEE Security & Privacy, Vol. 1, No. 2, pp. 33-42, 2003.
這篇論文中,我們提出使用entropy-box-counting (EBC) 的新方法來分析碎形維度且應用於虹膜辨識系統。
本論文以碎形維度來表示虹膜紋理區域資訊。在影像前置處理中,將環狀的虹膜區域正規化轉換成矩形的區塊並且將此區塊再分成48個小區塊,然後計算這些區塊的碎形維度值並將其串連在一起作為一個虹膜特徵向量。我們以空間距離來測量兩個虹膜的相似程度,以最小距離分類器 (MDC) 來確定兩虹膜是否屬於相同類別。最後我們以CASIA資料庫所儲存之虹膜對系統進行測試,得到虹膜辨識率為97.69%和等錯誤率為11.57%,證明我們系統具有高度的有效性。

In this thesis, we propose a new method of fractal analysis using entropy-box-counting (EBC) for automatic iris recognition. First, the annular iris image is normalized into a rectangular iris image and divided into forty-eight blocks. We calculate the fractal dimension values for these image blocks and then concatenate all these features together as the iris feature vector. The similarity of two irises can be measured by the spatial distance. We use the Minimum Distance Classifier (MDC) to determine whether two irises belong to the same class. Experimental results show that the recognition rate is 97.69% and the equal error rate is 11.57% for the images in CASIA database.
其他識別: U0005-1807200718494200
Appears in Collections:資訊科學與工程學系所

Show full item record

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.