用于虹膜识别的旋转角度估计算法研究
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摘要
虹膜图像采集时,由于人脸的偏转或是眼珠的转动,造成采集到的虹膜图像与原始虹膜数据库中的图像有旋转夹角产生。本文对如何估计两幅虹膜图像间的旋转夹角这一课题进行了初步的研究,提出了两种基于图像矩的旋转角度估计算法。
根据几何矩的物理特性知某区域的“取向”可由此区域主轴与水平轴的夹角表示,主轴则是关于此区域转动惯量最小的一条直线。因此某一区域关于水平轴的旋转角度可以理解为这一区域的“取向”。基于几何矩描述的旋转角度估计算法将分割后的眼睛区域作为目标区域,眼睛区域关于水平轴的旋转角度则可以通过计算该区域最小转动惯量得到。
由于Zernike矩具有旋转不变性,旋转后的虹膜区域与未旋转的虹膜区域的Zernike矩模值是相同的,只存在相位角的偏差,因此则可以通过计算两幅图像Zernike矩值对应的相位角之差估计其相对旋转角度。基于Zernike矩的角度估计算法将定位后虹膜区域的灰度图像作为目标区域,并将其映射到单位圆上,然后计算其Zernike矩值所对应的相位角,通过与模板图像Zernike矩所对应相位角之间的数学运算得到两幅图像之间的相对虹膜旋转角度。
实验结果表明,两种算法均能较准确估计出虹膜的旋转角度,对系统性能的提升有一定的帮助。
On the collection of iris image, there is a rotate angle between the collected iris image and the original iris image in the database because of the deflexion of the face or the turn of the eyeball. In this paper, the estimation of this rotate angle is researched, and two novel algorithms are proposed to calculate this angle based on the image moment.
According to the physical characteristic of the geometric moments, it is known that the orientation of a region can be represented by the angle between the horizontal axis and the principal axis of this region which is the line with the smallest turning inertia of the region. So the angle of a region rotated from the horizontal axis can be regarded as the orientation of this region. Making the segmented iris image as the target region, the geometric moments based angle estimating method can get the rotate angle of the iris by calculating the smallest turning inertia of the target region.
Because Zernike moment has rotation invariance, the primal iris image and the rotated iris image have the same Zernike moment modules except for the different phase angle. The relative rotate angle can be got by calculating the difference between the Zernike moments phase angles of different images. The Zernike moment based angle estimating algorithm regards the located iris gray image as the target region and maps it on the unit disk. Then the corresponding phase angel of the Zernike moment is computed and the relative rotate angle can be obtained according to the Zernike moment phase angles of two iris images.
The experimental results shown that both these two proposed algorithms can basically estimate the rotated angle and improve the performance of the iris recognition system.
根据几何矩的物理特性知某区域的“取向”可由此区域主轴与水平轴的夹角表示,主轴则是关于此区域转动惯量最小的一条直线。因此某一区域关于水平轴的旋转角度可以理解为这一区域的“取向”。基于几何矩描述的旋转角度估计算法将分割后的眼睛区域作为目标区域,眼睛区域关于水平轴的旋转角度则可以通过计算该区域最小转动惯量得到。
由于Zernike矩具有旋转不变性,旋转后的虹膜区域与未旋转的虹膜区域的Zernike矩模值是相同的,只存在相位角的偏差,因此则可以通过计算两幅图像Zernike矩值对应的相位角之差估计其相对旋转角度。基于Zernike矩的角度估计算法将定位后虹膜区域的灰度图像作为目标区域,并将其映射到单位圆上,然后计算其Zernike矩值所对应的相位角,通过与模板图像Zernike矩所对应相位角之间的数学运算得到两幅图像之间的相对虹膜旋转角度。
实验结果表明,两种算法均能较准确估计出虹膜的旋转角度,对系统性能的提升有一定的帮助。
On the collection of iris image, there is a rotate angle between the collected iris image and the original iris image in the database because of the deflexion of the face or the turn of the eyeball. In this paper, the estimation of this rotate angle is researched, and two novel algorithms are proposed to calculate this angle based on the image moment.
According to the physical characteristic of the geometric moments, it is known that the orientation of a region can be represented by the angle between the horizontal axis and the principal axis of this region which is the line with the smallest turning inertia of the region. So the angle of a region rotated from the horizontal axis can be regarded as the orientation of this region. Making the segmented iris image as the target region, the geometric moments based angle estimating method can get the rotate angle of the iris by calculating the smallest turning inertia of the target region.
Because Zernike moment has rotation invariance, the primal iris image and the rotated iris image have the same Zernike moment modules except for the different phase angle. The relative rotate angle can be got by calculating the difference between the Zernike moments phase angles of different images. The Zernike moment based angle estimating algorithm regards the located iris gray image as the target region and maps it on the unit disk. Then the corresponding phase angel of the Zernike moment is computed and the relative rotate angle can be obtained according to the Zernike moment phase angles of two iris images.
The experimental results shown that both these two proposed algorithms can basically estimate the rotated angle and improve the performance of the iris recognition system.
引文
[1] A .K. Jain, R.M. Bolle and S. Pankanti, et al. Biometrics: Personal Identification in a Networked Society. Norwell, MA: Kluwer. 1999.
[2] J. Daugman. High Confidence Visual Recognition of Persons by a Test of Statistical Independence. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1993, 15(11):1148-1161.
[3] R. Wildes. Iris Recognition: An Emerging Biometric Technology. Proceedings of the IEEE. 1997, 85 (9):1348-1363.
[4] L. Flout and A. Safir. Iris Recognition System. United States Patent. No.4641349. 1987.
[5] T. Mansfield, G. Kelly, D. Chandler et al. Biometric Product Testing Final Report. Issue 1.0. National Physical Laboratory of UK. 2001.
[6]谭铁牛,朱勇,王蕴红.虹膜图像采集装置.实用新型专利.国家知识产权局专利号:CN_2392219U, 1999.
[7]谭铁牛,朱勇,王蕴红.基于虹膜识别的身份鉴定方法与装置.发明专利.国家知识产权局专利号:CN_1282048A, 1999.
[8] A. Mansfield and J. Wayman. Best Practice Standards for Testing and Reporting on Biometric Device Performance. National Physical Laboratory of UK. 2002.
[9] R.G. Johnson. Can Iris Patterns be used to identity People? Chemical and Laser Sciences Division LA-12331-PR. Los Alamos National Laboratory. Los Alamos, Calif. 1991.
[10] W W .Boles,"A Security System Based on Human’s Identification Using Wavelet Transform",1997 First International Conference on Knowledge-Based Intelligent Electronic Systems,pp .533-54
[11] J. Daugman. Biometric Personal Identification System Based on Iris Analysis. United States Patent. No.5291560. 1994.
[12] R. Wildes, J. C. Asmuth, G. Green, et al. A Machine-vision System for Iris Recognition, Machine Vision and Applications. Vol.9, pp.l-8. 1996.
[13] J. Daugman. How Iris Recognition Works. IEEE Transactions on Circuit and System for Video Technology, 2004, 14(1):21-30.
[14] T. Reed, J. M. Buf. A review of recent texture segmentation and feature extraction techniques. Computer Vision, Graphics, and Image Processing: Image Understanding. 1993, 57 (3) : 359～372.
[15]崔屹.图像处理与分析—数学形态学方法及应用,科学出版社, 2002.
[16]章毓晋.图像工程(上册),北京:清华大学出版社, 1999.
[17] M. Pardas. Extraction and Tracking of the Eyelids. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Istanbul. 2000: 2357-2360
[18] J. Daugman, Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns. International Journal of Computer Vision. Vo1.45, No. l, pp.25-38, 2001.
[19] J. Daugman. Demodulation by Complex-valued Wavelets for Stochastic Pattern Recognition. International Journal of Wavelets, Multi-resolution and Information Processing. Vol. 1, No. l, pp. I-17. 2003.
[20] J. Daugman. The Importance of Being Random: Statistical Principles of Iris Recognition. Pattern Recognition. Vol. 36, No. 2, pp.279-291. 2003.
[21] T. S. Lee. Image Representation Using 2D Gabor Wavelets. IEEE Trans. on Pattern Analysis and Machine Intelligence. Vo1.18, No.10, pp.959-971. 1996.
[22] D. Clausi, M. Jernigan. Designing Gabor Filters for Optimal Texture Separability. Pattern Recognition. Vo1.33, pp.1835-1849. 2000.
[23] J. Daugman and C. Downing. Demodulation, Predictive Coding, and Spatial vision. Journal of optical Society of America. Vo1.12, No.4. April 1995.
[24] J. Daugman. Quadrature-phase Simple-cell Pairs Are Appropriately Described in Complex Analytic Form. Journal of optical Society of America. Vol.10, No.2. 1993.
[25] J. Daugman. Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-Dimensional Visual Cortical Filters. Journal of Optical Society America. Vo1.2, pp.1160-1169. 1985.
[26] J. Daugman. Complete Discrete 2D Gabor Transforms by Neural Networks for Image Analysis and Compression. IEEE Trans. on Acoustics, Speech, and Signal Processing. Vol.36, No.7, pp.1169-1179. 1988.
[27] J. Daugman. Two-Dimensional Spectral Analysis of Cortical Receptive Field Profiles. Vision Research. Vol. 20, pp.847-856. 1979.
[28] A. Bovik, N.Gopal. Smooth and an Image Frequencies by Gabor Wavelets. No.3, pp. 691-712.1992.
[29] J. Malik and P. Perona. Preatentive Texture Discrimination with Early Vision Mechanisms. Journal of Optical Society of America A. Vo1.7, pp. 923-932. 1990.
[30] R. Sanchez-Reillo and C. Sanchez-Avil. Iris Recognition with low template size. Proc. of International Conference on Audio and Video-Based Biometric person Authentication. pp. 324-329. 2000.
[31]孙即祥图象分析科学出版社2005
[32] M-K Hu, "Pattern recognition by moments invariants", Proc. IRE, vol.49pp.1428, Sept 1961
[33] M. K. H u "Visual pattern recognition by moment invariants" IRE Trans on,information theory,vol.IT 8 ,pp .17 9-187,Fe b1968
[34] S .A .D udani,et al ,"Aircraft identification by moment invariants",IEEE Transon computers,vo I.C-26,No.l, pp .39-45,Ja nuary1 977
[35] E. Bigorgne, et al, "An invariant local vector for content-based image retrieval",1S'7'International conference on pattern recognition, vo l.l,pp.10 19-1022,2000
[36] S .M aitra,"Moment invariants",Proc.IEEE,vo1.67,pp .697-699,April 1979
[37] Sim D G,Kim H K and Park R H, "Invariant texture retrieval using modified Zernike moments",Image and Vision Computing,2004,22:331-342
[38] Marcin N and Reinhard K, "Shape retrieval using 3D Zernike descriptors",Computer-Aided Design,2004,36:1047-1062
[39] YE Bin , PEN GJia-xiong Analysis and improvement of invariance of Zernike moments
[40] Alireze X,Hong Yawnua,Invariant image recognition by Zernike moments.IEEE Trans on PAMI,1990,12(5),489~497
[41] A New High-Speed Algorithm for Center and Rotate Angle of Electronic Components。Proceedings of the 2006 IEEE International Conference on Mechatronics and Automation June 25 - 28, 2006, Luoyang, China
[42] Rozmus, etal. Method and Apparatus for Illuminating and Imaging Eyes through Eyeglasses. United States Patent, No. 6069967, 1997
[43] McKendall. Method of Verifying the Presence of an Eye in a Close-up Image. United StatesP atent,N o.60 28949, 1997
[44] Camus, M.SalganicofT, J. Chmielewski, et al. Method and Apparatus forRemoval of Bright or Dark Spots by the Fusion of Multiple Images. United States Patent, No 6088470, 1998
[45] Luo. Method and Apparatus for Determining the Position of Eyes and for Correcting Eye-defects in a Captured Frame. United States Patent, No.6 134339, 1998
[46] GZhang and M. Salganicoff. Method of Measuring the Focus of Close-Up Images of Eyes. United States Patent, No.5953440, 1999
[47] W.Lee, A.Yang, and J.Chae. Apparatus and Methodf or Adjusting Focus Position in Iris Recognition System. United States Patent, US20020131622A1, 2002C. Tisse, L. Martin, L. Torres and M. Robert. Person Identification Technique using Human Iris Recognition. Proc. of vision Interface. pp. 294-299. 2002.
[48] T. Tangsukson and J. Havlicek. AM-FM Image Segmentation. Proc. Of IEEE Inter Conf on Image Processing. pp. 104-107. 2000.
[49] J. P. Havlicek, D.S. Harding, and A.C. Bovik. Ibe Mutli-component AM-FM Image Representation. IEEE Trans. on Image Processing, special issue on nonlinear image processing. Vo1.5, No.6, pp. 1094-1100. 1996.
[50] J.E. Siedlarz. Iris: More Detailed than a Fingerprint. IEEE Spectrum. Vol.31, pp.27.1994.
[51]王蕴红,谭铁牛,朱勇.基于虹膜识别的身份鉴别.自动化学报. Vol.28, No.l, pp. 1-10. 2002.
[52]章品正.旋转人脸的模板匹配定位与旋转角度估算方法电路与系统学报2004.
[53]马力.基于虹膜识别的身份鉴别方法研究.北京:中科院自动化研究所. 2003.
[54]第一届中国生物特征识别竞赛训练库Ⅰ. http://www.sinobiometrics.com/sinobiometrics '04.htm. 2004-08.
[55] CASIA虹膜图像库. http://sinobiometrics.com/casiairis.htm. 2003-12.
[56] S. Li, K. Chan and C. Wang. Performance Evaluation of the Nearest Feature Line Method in Image Classification and Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vo1.22, No. 11, pp.1335-1339. 2000.
[57] R. Wildes, J. Asmuth, G. Green, et al. A Machine-vision System for Iris Recognition. Machine Vision and Applications. Vol.9, pp. 1-8. 1996.
[58] J. L. Cui, Y. H. Wang, T. N. Tan, et al. A fast and Robust Iris Localization Method Based on Texture Segmentation. SPIE Defense and Security Symposium, 2004.
[2] J. Daugman. High Confidence Visual Recognition of Persons by a Test of Statistical Independence. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1993, 15(11):1148-1161.
[3] R. Wildes. Iris Recognition: An Emerging Biometric Technology. Proceedings of the IEEE. 1997, 85 (9):1348-1363.
[4] L. Flout and A. Safir. Iris Recognition System. United States Patent. No.4641349. 1987.
[5] T. Mansfield, G. Kelly, D. Chandler et al. Biometric Product Testing Final Report. Issue 1.0. National Physical Laboratory of UK. 2001.
[6]谭铁牛,朱勇,王蕴红.虹膜图像采集装置.实用新型专利.国家知识产权局专利号:CN_2392219U, 1999.
[7]谭铁牛,朱勇,王蕴红.基于虹膜识别的身份鉴定方法与装置.发明专利.国家知识产权局专利号:CN_1282048A, 1999.
[8] A. Mansfield and J. Wayman. Best Practice Standards for Testing and Reporting on Biometric Device Performance. National Physical Laboratory of UK. 2002.
[9] R.G. Johnson. Can Iris Patterns be used to identity People? Chemical and Laser Sciences Division LA-12331-PR. Los Alamos National Laboratory. Los Alamos, Calif. 1991.
[10] W W .Boles,"A Security System Based on Human’s Identification Using Wavelet Transform",1997 First International Conference on Knowledge-Based Intelligent Electronic Systems,pp .533-54
[11] J. Daugman. Biometric Personal Identification System Based on Iris Analysis. United States Patent. No.5291560. 1994.
[12] R. Wildes, J. C. Asmuth, G. Green, et al. A Machine-vision System for Iris Recognition, Machine Vision and Applications. Vol.9, pp.l-8. 1996.
[13] J. Daugman. How Iris Recognition Works. IEEE Transactions on Circuit and System for Video Technology, 2004, 14(1):21-30.
[14] T. Reed, J. M. Buf. A review of recent texture segmentation and feature extraction techniques. Computer Vision, Graphics, and Image Processing: Image Understanding. 1993, 57 (3) : 359～372.
[15]崔屹.图像处理与分析—数学形态学方法及应用,科学出版社, 2002.
[16]章毓晋.图像工程(上册),北京:清华大学出版社, 1999.
[17] M. Pardas. Extraction and Tracking of the Eyelids. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Istanbul. 2000: 2357-2360
[18] J. Daugman, Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns. International Journal of Computer Vision. Vo1.45, No. l, pp.25-38, 2001.
[19] J. Daugman. Demodulation by Complex-valued Wavelets for Stochastic Pattern Recognition. International Journal of Wavelets, Multi-resolution and Information Processing. Vol. 1, No. l, pp. I-17. 2003.
[20] J. Daugman. The Importance of Being Random: Statistical Principles of Iris Recognition. Pattern Recognition. Vol. 36, No. 2, pp.279-291. 2003.
[21] T. S. Lee. Image Representation Using 2D Gabor Wavelets. IEEE Trans. on Pattern Analysis and Machine Intelligence. Vo1.18, No.10, pp.959-971. 1996.
[22] D. Clausi, M. Jernigan. Designing Gabor Filters for Optimal Texture Separability. Pattern Recognition. Vo1.33, pp.1835-1849. 2000.
[23] J. Daugman and C. Downing. Demodulation, Predictive Coding, and Spatial vision. Journal of optical Society of America. Vo1.12, No.4. April 1995.
[24] J. Daugman. Quadrature-phase Simple-cell Pairs Are Appropriately Described in Complex Analytic Form. Journal of optical Society of America. Vol.10, No.2. 1993.
[25] J. Daugman. Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-Dimensional Visual Cortical Filters. Journal of Optical Society America. Vo1.2, pp.1160-1169. 1985.
[26] J. Daugman. Complete Discrete 2D Gabor Transforms by Neural Networks for Image Analysis and Compression. IEEE Trans. on Acoustics, Speech, and Signal Processing. Vol.36, No.7, pp.1169-1179. 1988.
[27] J. Daugman. Two-Dimensional Spectral Analysis of Cortical Receptive Field Profiles. Vision Research. Vol. 20, pp.847-856. 1979.
[28] A. Bovik, N.Gopal. Smooth and an Image Frequencies by Gabor Wavelets. No.3, pp. 691-712.1992.
[29] J. Malik and P. Perona. Preatentive Texture Discrimination with Early Vision Mechanisms. Journal of Optical Society of America A. Vo1.7, pp. 923-932. 1990.
[30] R. Sanchez-Reillo and C. Sanchez-Avil. Iris Recognition with low template size. Proc. of International Conference on Audio and Video-Based Biometric person Authentication. pp. 324-329. 2000.
[31]孙即祥图象分析科学出版社2005
[32] M-K Hu, "Pattern recognition by moments invariants", Proc. IRE, vol.49pp.1428, Sept 1961
[33] M. K. H u "Visual pattern recognition by moment invariants" IRE Trans on,information theory,vol.IT 8 ,pp .17 9-187,Fe b1968
[34] S .A .D udani,et al ,"Aircraft identification by moment invariants",IEEE Transon computers,vo I.C-26,No.l, pp .39-45,Ja nuary1 977
[35] E. Bigorgne, et al, "An invariant local vector for content-based image retrieval",1S'7'International conference on pattern recognition, vo l.l,pp.10 19-1022,2000
[36] S .M aitra,"Moment invariants",Proc.IEEE,vo1.67,pp .697-699,April 1979
[37] Sim D G,Kim H K and Park R H, "Invariant texture retrieval using modified Zernike moments",Image and Vision Computing,2004,22:331-342
[38] Marcin N and Reinhard K, "Shape retrieval using 3D Zernike descriptors",Computer-Aided Design,2004,36:1047-1062
[39] YE Bin , PEN GJia-xiong Analysis and improvement of invariance of Zernike moments
[40] Alireze X,Hong Yawnua,Invariant image recognition by Zernike moments.IEEE Trans on PAMI,1990,12(5),489~497
[41] A New High-Speed Algorithm for Center and Rotate Angle of Electronic Components。Proceedings of the 2006 IEEE International Conference on Mechatronics and Automation June 25 - 28, 2006, Luoyang, China
[42] Rozmus, etal. Method and Apparatus for Illuminating and Imaging Eyes through Eyeglasses. United States Patent, No. 6069967, 1997
[43] McKendall. Method of Verifying the Presence of an Eye in a Close-up Image. United StatesP atent,N o.60 28949, 1997
[44] Camus, M.SalganicofT, J. Chmielewski, et al. Method and Apparatus forRemoval of Bright or Dark Spots by the Fusion of Multiple Images. United States Patent, No 6088470, 1998
[45] Luo. Method and Apparatus for Determining the Position of Eyes and for Correcting Eye-defects in a Captured Frame. United States Patent, No.6 134339, 1998
[46] GZhang and M. Salganicoff. Method of Measuring the Focus of Close-Up Images of Eyes. United States Patent, No.5953440, 1999
[47] W.Lee, A.Yang, and J.Chae. Apparatus and Methodf or Adjusting Focus Position in Iris Recognition System. United States Patent, US20020131622A1, 2002C. Tisse, L. Martin, L. Torres and M. Robert. Person Identification Technique using Human Iris Recognition. Proc. of vision Interface. pp. 294-299. 2002.
[48] T. Tangsukson and J. Havlicek. AM-FM Image Segmentation. Proc. Of IEEE Inter Conf on Image Processing. pp. 104-107. 2000.
[49] J. P. Havlicek, D.S. Harding, and A.C. Bovik. Ibe Mutli-component AM-FM Image Representation. IEEE Trans. on Image Processing, special issue on nonlinear image processing. Vo1.5, No.6, pp. 1094-1100. 1996.
[50] J.E. Siedlarz. Iris: More Detailed than a Fingerprint. IEEE Spectrum. Vol.31, pp.27.1994.
[51]王蕴红,谭铁牛,朱勇.基于虹膜识别的身份鉴别.自动化学报. Vol.28, No.l, pp. 1-10. 2002.
[52]章品正.旋转人脸的模板匹配定位与旋转角度估算方法电路与系统学报2004.
[53]马力.基于虹膜识别的身份鉴别方法研究.北京:中科院自动化研究所. 2003.
[54]第一届中国生物特征识别竞赛训练库Ⅰ. http://www.sinobiometrics.com/sinobiometrics '04.htm. 2004-08.
[55] CASIA虹膜图像库. http://sinobiometrics.com/casiairis.htm. 2003-12.
[56] S. Li, K. Chan and C. Wang. Performance Evaluation of the Nearest Feature Line Method in Image Classification and Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vo1.22, No. 11, pp.1335-1339. 2000.
[57] R. Wildes, J. Asmuth, G. Green, et al. A Machine-vision System for Iris Recognition. Machine Vision and Applications. Vol.9, pp. 1-8. 1996.
[58] J. L. Cui, Y. H. Wang, T. N. Tan, et al. A fast and Robust Iris Localization Method Based on Texture Segmentation. SPIE Defense and Security Symposium, 2004.