基于单位扇环灰度的虹膜定位算法
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摘要
提出了基于单位扇环灰度的虹膜定位算法。利用投影法分割出一个包含瞳孔的矩形区域,通过最大类间方差法确定该矩形区域的阈值,完成瞳孔的分割。对于虹膜外边界的定位,需要基于瞳孔中心分割出某一方向上的扇形区域,以5个像素作为扇形区域的步长,计算每个扇环的平均灰度值,根据灰度变化情况就可以确定该方向上的虹膜外边界点。其他方向上的边界点也通过此方法确定。对这些边界点进行筛选并进行圆的拟合,最终实现虹膜的定位。实验结果表明,采用该方法所分割的虹膜图像可以获得良好的效果,具有良好的应用和参考价值。
A method of gray value of unit sector area is proposed to solve the problem of iris localization. A rectangular area containing pupil is segmented by gray-scale projection algorithm, the OTSU algorithm is used to determine the threshold of this rectangular area and segmented the pupil. With respect to outer location in a certain direction, a sector area in the direction needs to be segmented according to the pupil center. The gray value of each concentric sector ring is calculated by setting steps to 5 pixels,and the abscissa values of the position where the maximum change in gray value is calculated as the radius in this direction.The location of boundary points in other directions are also applied by this method. These points are filtered and used to fit outer boundary of iris. The results show that iris images segmented by this method can achieve a good result with good application and reference value.
A method of gray value of unit sector area is proposed to solve the problem of iris localization. A rectangular area containing pupil is segmented by gray-scale projection algorithm, the OTSU algorithm is used to determine the threshold of this rectangular area and segmented the pupil. With respect to outer location in a certain direction, a sector area in the direction needs to be segmented according to the pupil center. The gray value of each concentric sector ring is calculated by setting steps to 5 pixels,and the abscissa values of the position where the maximum change in gray value is calculated as the radius in this direction.The location of boundary points in other directions are also applied by this method. These points are filtered and used to fit outer boundary of iris. The results show that iris images segmented by this method can achieve a good result with good application and reference value.
引文
[1] Abdullah M A M, Dlay S S, Woo W L, et al. Robust Iris Segmentation Method Based on a New Active Contour Force With a Noncircular Normalization[J]. IEEE Transactions on Systems Man & Cybernetics Systems,2017,47(12):3128—3141.
[2] Jan F. Segmentation and localization schemes for non-ideal iris biometric systems[J]. Signal Processing,2017,133:192—212.
[3] Daugman J. Statistical richness of visual phase information: Update on recognizing persons by iris patterns[J]. International Journal of Computer Vision,2001,45(1):25—38.
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[5] Daugman J. The importance of being random: statistical principles of iris recognition [J]. Pattern Recognition,2003,36(2):279—291.
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[7] Ganorkar S R, Ghatol A A. Iris recognition: an emerging biometric technology[J]. Proceedings of the IEEE,1997,85(9):1348—1363.
[8] 程宇奇, 朱明, 李桂菊, 等. 应用迭代圆环像素率法实现快速虹膜定位[J]. 光学精密工程,2010,18(10):2306—2313.Cheng Yuqi, Zhu Ming, Li Guiju, et al. Rapid iris localization based on method of iterative pixel ratio to cirque area[J]. Optics and Precision Engineering,2010,18(10):2306—2313.
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[10] 刘帅, 刘元宁, 朱晓冬, 等. 基于分块搜索的虹膜定位算法[J]. 计算机工程与应用,2018,54(18):212—217.Liu Shuai, Liu Yuanning, Zhu Xiaodong, et al. Iris location algorithm based on partitioning search[J]. Computer Engine-ering and Applications,2018,54(18):212—217.
[11] 朱立军, 苑玮琦. 基于并查集和边缘检测模板的非理想虹膜定位[J]. 计算机应用研究,2018,35(6):1879—1882.Zhu Lijun, Yuan Weiqi. Non-ideal iris localization method based on union-find-set and edge detection template[J]. App-lication Research of Computers,2018,35(6):1879—188.
[12] 王延年, 赵朗月, 刘会敏. 基于模拟退火遗传算法的虹膜轮廓提取方法[J]. 激光杂志,2018,39(6):89—93.Wang Yannian, Zhao Langyue, Liu Huimin. An iris extraction method based on simulated annealing genetic algorithm[J]. Laser Journal,2018,39(6):89—93.
[13] Bakshi S, Sa P K, Majhi B. A novel phase-intensive local pattern for periocular recognition under visible spectrum[J]. Biocybernetics & Biomedical Engineering,2015,35(1):30—44.
[14] Sahu B, Sa P K, Bakshi S, et al. Reducing dense local feature key-points for faster iris recognition[J]. Computers & Electrical Engineering,2018,70:939—949.
[15] Nalla P R, Kumar A. Toward more accurate iris iecognition using cross-spectral matching[J]. IEEE Transactions on Image Processing,2016,26(1):208—221.
[16] Bergmüller T, Christopoulos E, Fehrenbach K, et al. Recompression effects in iris recognition[J]. Image & Vision Computing,2017,58:142—157.
[17] Barpanda S S, Sa P K, Marques O, et al. Iris recognition with tunable filter bank based feature[J]. Multimedia Tools & Applications,2018,77(6):7637—7674.
[18] Barpanda S S, Majhi B, Sa P K. Region-based feature extraction from non-cooperative iris images using CDF 9/7 filter bank[J]. Innovations in Systems & Software Engineering,2015,11(3):1—6.
[19] Bazrafkan S, Thavalengal S, Corcoran P. An end to end deep neural network for iris segmentation in unconstrained scenarios[J]. Neural Networks the Official Journal of the International Neural Network Society,2018,106:79—95.
[20] 李志明. 基于卷积神经网络的虹膜活体检测算法研究[J]. 计算机工程,2016,42(5):239—243.Li Zhiming. Research on iris liveness detection algorithm b-ased on convolutional neural network[J]. Computer Engineer-ing,2016,42(5):239—243.
[21] Ma L, Tan T, Wang Y, et al. Efficient iris recognition by characterizing key local variations[J]. IEEE Transactions on Image Processing,2004,13(6):739—750.
[22] Alvarez-Betancourt Y, Garcia-Silvente M. A keypoints-based feature extraction method for iris recognition under variable image quality conditions[J]. Knowledge-Based Systems,2016,92(C):169—182.
[23] Soliman N F, Mohamed E, Magdi F, et al. Efficient iris lo-calization and recognition[J]. Optik-International Journal for Light and Electron Optics,2017,140:469—475.
[24] Vanthana P S, Muthukumar A. Iris authentication using gray level co-occurrence matrix and hausdorff dimension[C]//2015 International Conference on Computer Communication and Informatics.Coimbatore,India:IEEE,2015:1—5.
[25] Sahmoud S A, Abuhaiba I S. Efficient iris segmentation me-thod in unconstrained environments[J]. Pattern Recognition, 2013,46(12):3174—3185.
[26] Subban R, Susitha N, Mankame D P. Efficient iris recogniti-on using haralick features based extraction and fuzzy partic-le swarm optimization[J]. Cluster Computing,2018,21(1):79—90.
[2] Jan F. Segmentation and localization schemes for non-ideal iris biometric systems[J]. Signal Processing,2017,133:192—212.
[3] Daugman J. Statistical richness of visual phase information: Update on recognizing persons by iris patterns[J]. International Journal of Computer Vision,2001,45(1):25—38.
[4] Daugman J. How iris recognition works[J]. IEEE Transactions on Circuitsand Systems for Video Technology,2004,14(1):21—30.
[5] Daugman J. The importance of being random: statistical principles of iris recognition [J]. Pattern Recognition,2003,36(2):279—291.
[6] Roy D A, Soni U S. IRIS segmentation using Daughman's method[C]// 2016 International Conference on Electrical, Electronics, and Optimization Techniques.Chennai,India:IEEE, 2016:2668—2676.
[7] Ganorkar S R, Ghatol A A. Iris recognition: an emerging biometric technology[J]. Proceedings of the IEEE,1997,85(9):1348—1363.
[8] 程宇奇, 朱明, 李桂菊, 等. 应用迭代圆环像素率法实现快速虹膜定位[J]. 光学精密工程,2010,18(10):2306—2313.Cheng Yuqi, Zhu Ming, Li Guiju, et al. Rapid iris localization based on method of iterative pixel ratio to cirque area[J]. Optics and Precision Engineering,2010,18(10):2306—2313.
[9] 初秀琴, 胡乐, 陈云飞, 等. 基于小范围边缘区域搜索的虹膜定位算法[J]. 光电工程,2010,37(11):135—139.Chu Xiuqin, Hu Le, Chen Yunfei, et al. Iris location based on searching small-scale edge regions[J]. Optp-Electronic En-gineering,2010,37(11):135—139.
[10] 刘帅, 刘元宁, 朱晓冬, 等. 基于分块搜索的虹膜定位算法[J]. 计算机工程与应用,2018,54(18):212—217.Liu Shuai, Liu Yuanning, Zhu Xiaodong, et al. Iris location algorithm based on partitioning search[J]. Computer Engine-ering and Applications,2018,54(18):212—217.
[11] 朱立军, 苑玮琦. 基于并查集和边缘检测模板的非理想虹膜定位[J]. 计算机应用研究,2018,35(6):1879—1882.Zhu Lijun, Yuan Weiqi. Non-ideal iris localization method based on union-find-set and edge detection template[J]. App-lication Research of Computers,2018,35(6):1879—188.
[12] 王延年, 赵朗月, 刘会敏. 基于模拟退火遗传算法的虹膜轮廓提取方法[J]. 激光杂志,2018,39(6):89—93.Wang Yannian, Zhao Langyue, Liu Huimin. An iris extraction method based on simulated annealing genetic algorithm[J]. Laser Journal,2018,39(6):89—93.
[13] Bakshi S, Sa P K, Majhi B. A novel phase-intensive local pattern for periocular recognition under visible spectrum[J]. Biocybernetics & Biomedical Engineering,2015,35(1):30—44.
[14] Sahu B, Sa P K, Bakshi S, et al. Reducing dense local feature key-points for faster iris recognition[J]. Computers & Electrical Engineering,2018,70:939—949.
[15] Nalla P R, Kumar A. Toward more accurate iris iecognition using cross-spectral matching[J]. IEEE Transactions on Image Processing,2016,26(1):208—221.
[16] Bergmüller T, Christopoulos E, Fehrenbach K, et al. Recompression effects in iris recognition[J]. Image & Vision Computing,2017,58:142—157.
[17] Barpanda S S, Sa P K, Marques O, et al. Iris recognition with tunable filter bank based feature[J]. Multimedia Tools & Applications,2018,77(6):7637—7674.
[18] Barpanda S S, Majhi B, Sa P K. Region-based feature extraction from non-cooperative iris images using CDF 9/7 filter bank[J]. Innovations in Systems & Software Engineering,2015,11(3):1—6.
[19] Bazrafkan S, Thavalengal S, Corcoran P. An end to end deep neural network for iris segmentation in unconstrained scenarios[J]. Neural Networks the Official Journal of the International Neural Network Society,2018,106:79—95.
[20] 李志明. 基于卷积神经网络的虹膜活体检测算法研究[J]. 计算机工程,2016,42(5):239—243.Li Zhiming. Research on iris liveness detection algorithm b-ased on convolutional neural network[J]. Computer Engineer-ing,2016,42(5):239—243.
[21] Ma L, Tan T, Wang Y, et al. Efficient iris recognition by characterizing key local variations[J]. IEEE Transactions on Image Processing,2004,13(6):739—750.
[22] Alvarez-Betancourt Y, Garcia-Silvente M. A keypoints-based feature extraction method for iris recognition under variable image quality conditions[J]. Knowledge-Based Systems,2016,92(C):169—182.
[23] Soliman N F, Mohamed E, Magdi F, et al. Efficient iris lo-calization and recognition[J]. Optik-International Journal for Light and Electron Optics,2017,140:469—475.
[24] Vanthana P S, Muthukumar A. Iris authentication using gray level co-occurrence matrix and hausdorff dimension[C]//2015 International Conference on Computer Communication and Informatics.Coimbatore,India:IEEE,2015:1—5.
[25] Sahmoud S A, Abuhaiba I S. Efficient iris segmentation me-thod in unconstrained environments[J]. Pattern Recognition, 2013,46(12):3174—3185.
[26] Subban R, Susitha N, Mankame D P. Efficient iris recogniti-on using haralick features based extraction and fuzzy partic-le swarm optimization[J]. Cluster Computing,2018,21(1):79—90.