[1]SHI Junpeng,WU Yiquan.A fingerprint minutiae matching algorithm based on chaotic bee colony optimization[J].CAAI Transactions on Intelligent Systems,2016,11(5):613-618.[doi:10.11992/tis.201601038]
Copy

A fingerprint minutiae matching algorithm based on chaotic bee colony optimization

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 5 Page number: 613-618 Column: 学术论文—机器学习 Public date: 2016-11-01
Title:
A fingerprint minutiae matching algorithm based on chaotic bee colony optimization
Author(s):
SHI Junpeng1;  WU Yiquan1; 2
1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
2. Jiangsu Key Laboratory of Image and Video Understanding for Social Safety, Nanjing University of Science and Technology, Nanjing 210094, China
Keywords:
fingerprint recognition; minutiae matching; swarm intelligence optimization; artificial bee colony; chaos strategy; variable boundary box; fitness function; polar coordinates
CLC:
TP391.4
DOI:
10.11992/tis.201601038
Abstract:
In order to further improve the operational speed and the recognition efficiency of fingerprint matching algorithms, a fingerprint matching algorithm based on chaotic bee colony activity and a variable boundary box was proposed. Firstly, by combining the advantages of artificial bee colony optimization including fast convergence times, fewer control parameters, and the lack of local optima, with the features of a chaos strategy including its random-like property and ergodicity, the chaotic bee colony activity was introduced into point pattern matching for fingerprint images. A corresponding fitness function incorporating both matching accuracy and operational time was then designed. The corresponding fitness function was then used to estimate the geometric transformation parameters for fingerprint rough matching. Finally, a variable boundary box can be used for fine matching, because it avoids any influences relating to local deformation of the fingerprint images. A large number of experimental results show that, compared with two alternative fingerprint matching algorithms (based on local features and genetic algorithm optimization, respectively) the proposed algorithm has a shorter operational time and has higher matching accuracy.
References:
[1] ITO K, NAKAJIMA H, KOBAYASHI K, et al. A fingerprint matching algorithm using phase-only correlation[J]. IEICE transactions on fundamentals of electronics, communications and computer sciences, 2004, E87-A(3):682-691.
[2] HE Yuliang, TIAN Jie, LI Liang, et al. Fingerprint matching based on global comprehensive similarity[J]. IEEE transactions on pattern analysis and machine intelligence, 2006, 28(6):850-862.
[3] LUMINI A, NANNI L. Two-class fingerprint matcher[J]. Pattern recognition, 2006, 39(4):714-716.
[4] JIANG Xudong, YAU W Y. Fingerprint minutiae matching based on the local and global structures[C]//Proceedings of the 15th International Conference on Pattern Recognition. Barcelona, Spain, 2000, 2:1038-1041.
[5] 于明, 皮海龙, 王岩, 等. 基于k近邻法和脊线追踪的指纹匹配算法[J]. 吉林大学学报:工学版, 2014, 44(6):1806-1810. YU Ming, PI Hailong, WANG Yan, et al. Fingerprint matching algorithm based on k-nearest neighbor and ridge line tracking methods[J]. Journal of Jilin university:engineering and technology edition, 2014, 44(6):1806-1810.
[6] 曹国, 孙权森, 毛志红, 等. 一种新的形变指纹匹配方法[J]. 中国图象图形学报, 2010, 15(4):645-649. CAO Guo, SUN Quansen, MAO Zhihong, et al. A new algorithm for distorted fingerprint matching[J]. Journal of image and graphics, 2010, 15(4):645-649.
[7] 袁东锋, 杜恒, 秦小铁. 基于三角形局部特征点模型指纹匹配算法[J]. 重庆师范大学学报:自然科学版, 2013, 30(2):69-73. YUAN Dongfeng, DU Heng, QIN Xiaotie. Fingerprint matching algorithm based on local triangular feature point model[J]. Journal of Chongqing normal university:nature science, 2013, 30(2):69-73.
[8] ZHANG Qing, YIN Yilong, YANG Gongping. Unmatched minutiae:useful information to boost fingerprint recognition[J]. Neurocomputing, 2016, 171:1401-1413.
[9] CAO Kai, YANG Xin, CHEN Xinjian, et al. Minutia handedness:a novel global feature for minutiae-based fingerprint matching[J]. Pattern recognition letters, 2012, 33(10):1411-1421.
[10] 朱宁, 施荣华, 吴科桦. 一种新的点模式指纹匹配方法[J]. 计算机工程与应用, 2006, 42(5):74-76. ZHU Ning, SHI Ronghua, WU Kehua. A new fingerprint matching method of minutiae[J]. Computer engineering and applications, 2006, 42(5):74-76.
[11] 魏鸿磊, 张文孝, 华顺刚. 一种采用脊线特征的指纹模糊匹配方法[J]. 智能系统学报, 2012, 7(3):235-240. WEI Honglei, ZHANG Wenxiao, HUA Shungang. A fuzzy fingerprint matching method based on ridge features[J]. CAAI transactions on intelligent systems, 2012,7(3):235-240.
[12] LIU Feng, ZHANG D. 3D fingerprint reconstruction system using feature correspondences and prior estimated finger model[J]. Pattern recognition, 2014, 47(1):178-193.
[13] 漆远, 田捷, 邓翔. 基于遗传算法的指纹图匹配算法及应用[J]. 软件学报, 2000, 11(4):488-493. QI Yuan, TIAN Jie, DENG Xiang. Genetic algorithm based fingerprint matching algorithm and its application in automated fingerprint recognition system[J]. Journal of software, 2000, 11(4):488-493.
[14] SHENG W, HOWELLS G, FAIRHUST M C, et al. Consensus fingerprint matching with genetically optimised approach[J]. Pattern recognition, 2009, 42(7):1399-1407.
[15] GHAZVINI M, SUFIKARIMI H, MOHAMMADI K. Fingerprint matching using genetic algorithm and triangle descriptors[C]//Proceedings of the 19th Iranian Conference on Electrical Engineering. Tehran, Iran, 2011:1-6.
[16] 吴一全, 张金矿. 基于Tent映射混沌粒子群的快速指纹特征匹配[J]. 信号处理, 2011, 27(2):168-173. WU Yiquan, ZHANG Jinkuang. Fast fingerprint minutiae matching based on Tent map chaotic particle swarm optimization[J]. Sigal processing, 2011, 27(2):168-173.
[17] KARABOGA D, BASTURK B. On the performance of artificial bee colony (ABC) algorithm[J]. Applied soft computing, 2008, 8(1):687-697.
[18] KARABOGA D, OZTURK C. A novel clustering approach:Artificial bee colony (ABC) algorithm[J]. Applied soft computing, 2011, 11(1):652-657.
[19] 秦全德, 程适, 李丽, 等. 人工蜂群算法研究综述[J]. 智能系统学报, 2014, 9(2):127-135. QIN Quande, CHENG Shi, LI Li, et al. Artificial bee colony algorithm:a survey[J]. CAAI transactions on intelligent systems, 2014, 9(2):127-135.
[20] 吴一全, 王凯, 曹鹏祥. 蜂群优化的二维非对称Tsallis交叉熵图像阈值选取[J]. 智能系统学报, 2015, 10(1):103-112. WU Yiquan, WANG Kai, CAO Pengxiang. Two-dimensional asymmetric tsallis cross entropy image threshold selection using bee colony optimization[J]. CAAI transactions on intelligent systems, 2015, 10(1):103-112.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems