基于高斯拉普拉斯算子与自适应优化伽柏滤波的虹膜识别

doi:10.13229/j.cnki.jdxbgxb20170605

摘要/Abstract

摘要： 为了抑制虹膜噪声并提高算法通用性,提出在虹膜识别中运用高斯拉普拉斯(Log)算子与自适应优化伽柏(Gabor)滤波。Log算子抑制虹膜噪声,40组频率和方向各不同的Gabor滤波提取虹膜特征,将特征转化为二进制特征编码。滤波参数用变异粒子群优化(MPSO)算法针对不同虹膜库进行自适应优化。通过计算虹膜间的汉明(Hamming)距离判定虹膜类别。与其他Gabor滤波和机器学习类算法相比,该算法可以有效抑制虹膜噪声干扰进而提高识别正确率,同时算法在多种虹膜库识别的通用性更好。

关键词: 计算机应用, 虹膜识别, 高斯拉普拉斯算子, 自适应优化伽柏滤波, 变异粒子群优化

Abstract: To suppress iris noise and improve algorithm versatility, it is proposed to use Laplacian of Gaussian (LOG) operator and adaptive optimization Gabor filtering in iris recognition. The LOG operator suppresses iris noise and 40 sets of Gabor filtering with different frequencies and directions extract iris features and transform the features into binary feature codes. The filtering parameters are adaptively optimized for different iris libraries using Mutation Particle Swarm Optimization (MPSO) algorithm. The iris category is determined by calculating the Hamming distance between irises. Compared with other Gabor filtering and machine learning algorithm, the proposed algorithm can effectively suppress iris noise interference and improve recognition accuracy. At the same time, this algorithm has higher versatility in recognition of various iris libraries.

Key words: computer application, iris recognition, Laplacian of Gaussian operator, adaptive optimization Gabor filtering, mutation particle swarm optimization

中图分类号:

TP391

刘元宁, 刘帅, 朱晓冬, 陈一浩, 郑少阁, 沈椿壮. 基于高斯拉普拉斯算子与自适应优化伽柏滤波的虹膜识别[J]. 吉林大学学报(工学版), 2018, 48(5): 1606-1613.

LIU Yuan-ning, LIU Shuai, ZHU Xiao-dong, CHEN Yi-hao, ZHENG Shao-ge, SHEN Chun-zhuang. LOG operator and adaptive optimization Gabor filtering for iris recognition[J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1606-1613.

参考文献

[1] 李星光,孙哲南,谭铁牛.虹膜图像质量评价综述[J].中国图象图形学报,2014, 19(6):813-824.
Li Xing-guang, Sun Zhe-nan, Tan Tie-niu.Overview of iris image quality-assessment[J]. Journal of Image and Graphics,2014, 19(6):813-824.
[2] 史春蕾, 周凤文, 胡雨露, 等. 虹膜图像的质量评价研究[J]. 液晶与显示, 2016, 31(12): 1131-1136.
Shi Chun-lei, Zhou Feng-wen, Hu Yu-lu, et al.Study for iris image quality assessment[J]. Chinese Journal of Liquid Crystals and Displays,2016, 31(12): 1131-1136.
[3] Liu Shuai, Liu Yuan-ning, Zhu Xiao-dong, et al.Iris double recognition based on modified evolutionary neural network[J]. Journal of Electronic Imaging, 2017,26(6): 063023
[4] Daugman J.High confidence visual recognition of persons by a test of statistical independence[J].IEEE Transactions,1993, 15(11):1148-1161.
[5] 李欢利,郭立红,王心醉,等.基于加权Gabor滤波器的虹膜识别[J].吉林大学学报:工学版, 2014,44(1):196-202.
Li Huan-li, Guo Li-hong, Wang Xin-zui,et al.Iris recognition based on weighted Gabor filte[J].Journal of Jilin University (Engineering and Technology Edition), 2014,44(1):196-202.
[6] Field D J.Relations between the statistics of natural images and the response properties of cortical cells[J]. Journal of the Optical Society of America A: Optics Image Science and Vision, 1987, 4(12): 2379-2394.
[7] 唐荣年,韩九强,张新曼.一种Log-Gabor滤波器结合多分辨率分析的虹膜识别方法[J].西安交通大学学报,2009,43(4):30-33.
Tang Rong-nian, Han Jiu-qiang, Zhang Xin-man.An iris recognition method using Log-Gabor filter and multi-resolution analysis[J].Journal of Xi'an Jiaotong University,2009,43(4):30-33.
[8] Zhou Jia-rui, Ji Zhen, Shen Lin-lin, et al.PSO based memetic algorithm for face recognition Gabor filters selection[C]∥IEEE Workshop on Memetic Computing, Paris, France,2011:1-6.
[9] Gao Si, Zhu Xiao-dong, Liu Yuan-ning, et al.A quality assessment method of iris image based on support vector machine[J]. Journal of Fiber Bioengineering & Informatics, 2015,8(2):293-300.
[10] 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.
[11] Daugman J.How iris recognition works[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2004, 14(1):21-30.
[12] Liu Shuai, Liu Yuan-ning, Zhu Xiao-dong,et al.Iris recognition based on adaptive gabor filte[R][C]∥12th Chinese Conference on Biometric Recognition. Germany:Springer, 2017:383-390.
[13] 朱维文,赵跃进,张镜水,等.基于被动太赫兹波图像的图像分割技术[J].北京理工大学学报, 2013, 33(4): 399-402.
Zhu Wei-wen, Zhao Yue-jin, Zhang Jing-shui, et al.Image segmentation of passive THz image[J].Transactions of Beijing Institute of Technology,2013,33(4):399-402.
[14] 田启川. 虹膜识别原理及算法[M].北京:国防工业出版社,2010:96-110.
[15] 李欢利,郭立红,李小明,等.基于统计特征中心对称局部二值模式的虹膜识别[J].光学精密工程,2013, 21(8): 2129-2136.
Li Huan-li, Guo Li-hong, Li Xiao-ming, et al.Iris recognition based on SCCS-LBP[J].Optics and Precision Engineering,2013, 21(8): 2129-2136.
[16] 张震,张英杰.基于支持向量机与Hamming距离的虹膜识别方法[J].郑州大学学报:工学版,2015,36(3):25-29.
Zhang Zhen, Zhang Ying-jie.Iris recognition method based on support vector machine and Hamming distance[J].Journal of Zhengzhou University(Engineering Science),2015,36(3):25-29.
[17] Megala T, Kashyap M, Vivekanandan K. Clonal selection algorithm with adaptive lévy mutation operator[C]∥ACM International Conference Proceeding Series, India, 2016: Article No.127.
[18] 张军. 计算智能[M].北京:清华大学出版社,2010:107-130.
[19] 赵天明. 虹膜特征提取方法研究[D].长春:吉林大学计算机科学与技术学院,2016.
Zhao Tian-ming.Research on iris feature extraction[D]. Changchun: College of Computer Science and Technology, Jilin University, 2016.
[20] 张震,刘博,李龙.一种多特征提取及融合的虹膜识别方法[J].郑州大学学报:工学版,2017,38(1):63-67.
Zhang Zhen, Liu Bo, Li Long.An iris recognition algorithm of multiple features extraction and fusion[J].Journal of Zhengzhou University(Engineering Science),2017,38(1):63-67.
[21] 田耘, 甄雯, 赵海军. 基于改进的SIFT算子和SVM分类器的瞳孔中心定位[J]. 液晶与显示, 2017, 32(6): 499-505.
Tian Yun, Zhen Wen, Zhao Hai-jun.Accurate pupil center location with SIFT descriptor and SVM classifie[J]. Chinese Journal of Liquid Crystals and Displays, 2017, 32(6): 499-505.
[22] He Fei, Han Ye, Wang Han, et al.Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network[J].Journal of Electronic Imaging, 2017,26(2): 023005-023005.
[23] Yu Zhi-wen, Chen Han-tao, You Jane, et al.Hybrid K nearest neighbor classifie[J].IEEE Transactions on Cybernetics, 2016, 46(6):1263-1275.
[24] 韩晓艳, 赵东. 基于粒子群的支持向量机图像识别[J]. 液晶与显示, 2017, 32(1): 69-75.
Han Xiao-yan, Zhao Dong.Support vector machine image recognition based on particle swarm[J]. Chinese Journal of Liquid Crystals and Displays, 2017, 32(1): 69-75.
[25] 张国梁, 贾松敏, 张祥银, 等. 采用自适应变异粒子群优化SVM的行为识别[J]. 光学精密工程, 2017, 25(6): 1669-1678.
Zhang Guo-liang, Jia Song-min, Zhang Xiang-yin, et al.Action recognition based on adaptive mutation particle swarm optimization for SVM[J]. Optics and Precision Engineering, 2017, 25(6): 1669-1678.
[26] 王晓辉, 吴禄慎, 陈华伟, 等. 应用改进的粒子群优化模糊聚类实现点云数据的区域分割[J]. 光学精密工程, 2017, 25(4): 1095-1105.
Wang Xiao-hui, Wu Lu-shen, Chen Hua-wei, et al.Region segmentation of point cloud data based on improved particle swarm optimization fuzzy clustering[J]. Optics and Precision Engineering, 2017, 25(4): 1095-1105.
[27] 吉林大学生物识别与信息安全技术实验室.虹膜库[EB/OL].[2017-01-22].http:∥www.jlucomputer.com
[28] Olanrewaju O A, Mbohwa C.Evaluating factors responsible for energy consumption: connection weight approach[C]∥IEEE Electrical Power and Energy Conference, Canada,2016:1-5.
[29] 中国科学院自动化研究所.共享虹膜库[EB/OL]. [2017-01-22].http:∥www.cbsr.ia.ac.cn/china/Iris%20Databases%20CH.asp
[30] 董宏兴. 基于自适应Gabor滤波的虹膜特征提取与识别方法研究[D].长春:吉林大学计算机科学与技术学院,2016.
Dong Hong-xing.Research on iris feature extraction and recognition based on the adaptive Gabor filtering[D]. Changchun: College of Computer Science and Technology, Jilin University, 2016.

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