Su Dan1,2, Wang Xinqiang1, Liu Yuhang2, Lu Yaopeng2, Li Ting1,2, Nie Zedong2#*
1(School of Electronic Engineering and Automation,Guilin University of Electronic Technology, Guilin 541004,China) 2(Shenzhen Institute of Advanced Technology,Chinese Academy of Science,Shenzhen 518055,Guangdong, China)
Abstract：Finger vein recognition has become a research hotspot in the field of identity recognition due to its advantages of high anti-counterfeiting, uniqueness, stability, and liveness detection. At present, most recognition algorithms based on vein structure features consider the feature of detail points, but it is easy to ignore the curve feature of the vein network structure, which would cause the loss of part of structural information and affect the recognition results. To solve the problems, this paper proposed a finger vein recognition algorithm based on curve descriptor. Firstly, the skeleton structure of finger veins was extracted, the intersection points and endpoints of the veins were detected, and the intersection points and endpoints were used to divide the vein skeleton into several curve segments. Secondly, the curve arc descriptors and intersecting arc descriptor were proposed based on the relative position and shape characteristics of the intersection points and curve segments, and the structural feature matrix of finger veins was extracted. Finally, the matching intersecting arc pair was calculated according to the weighted distance formula proposed, and then the degree of image matching was judged. Experiments were carried out on a finger vein database with a sample size of 840 images. The experimental results showed that the equal error rate of the LBP, LTP and SURF algorithms was 4.47%, 3.99% and 6.08% respectively, while the equal error rate of the proposed method was only 1.63% that was lower than that of LBP, LTP and SURF algorithms, indicating that the method in this paper had a certain universality and application prospect in finger vein recognition.
作者简介: #中国生物医学工程学会会员(Member,Chinese Society of Biomedical Engineering)
引用本文:
苏丹, 王新强, 刘宇航, 陆瑶芃, 李婷, 聂泽东. 基于曲线描述子的手指静脉识别[J]. 中国生物医学工程学报, 2022, 41(4): 420-430.
Su Dan, Wang Xinqiang, Liu Yuhang, Lu Yaopeng, Li Ting, Nie Zedong. Finger Vein Recognition Based on Curve Descriptor. Chinese Journal of Biomedical Engineering, 2022, 41(4): 420-430.
[1] 张丽萍,李卫军,宁欣,等. 一种基于2DHOL特征与(2D)2FPCA结合的手指静脉识别方法[J].计算机辅助设计与图形学学报,2018,30(2):254-261. [2] Jain AK, Chen Y, Demirkus M. Pores and ridges: high-resolution fingerprint matching using level 3 features[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 29(1): 15-27. [3] Zou Jie, Ji Qiang, Nagy G. A comparative study of local matching approach for face recognition[J]. IEEE Transactions on Image Processing, 2007, 16(10): 2617-2628. [4] Laadjel M, Bouridane A, Kurugollu F, et al. Palmprint recognition using Fisher-Gabor feature extraction[C]//2008 IEEE International Conference on Acoustics, Speech and Signal Processing. Las Vegas: IEEE, 2008: 1709-1712. [5] Li Ma, Tan Tieniu, Wang Yunhong, et al. Personal identification based on iris texture analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(12): 1519-1533. [6] Kumar A, Zhou Yingbo. Human identification using finger images[J]. IEEE Transactions on Image Processing, 2011, 21(4): 2228-2244. [7] 李杰,史艺丹,聂泽东,等. 手指静脉图像感兴趣区域快速提取方法研究[J]. 电子测量与仪器学报,2020,34(4):42-49. [8] Wu JianDa, Liu CT. Finger-vein pattern identification using principal component analysis and the neural network technique[J]. Expert Systems with Applications, 2011, 38(5):5423-5427. [9] 丁一军. 指静脉图像特征提取方法的研究[D]. 合肥:安徽大学,2019. [10] 彭加亮. 基于手指多模态生物特征的身份认证关键问题研究[D]. 哈尔滨:哈尔滨工业大学,2014. [11] 管凤旭,王科俊,刘靖宇,等. 归一双向加权(2D)2 PCA的手指静脉识别方法[J]. 模式识别与人工智能,2011,24(3):417-424. [12] Qin H, El-Yacoubi MA. Deep representation-based feature extraction and recovering for finger-vein verification[J]. IEEE Transactions on Information Forensics and Security, 2017, 12(8): 1816-1829. [13] Yang Wenming, Hui Changqing, Chen Zhiquan, et al. FV-GAN: finger vein representation using generative adversarial networks[J].IEEE Transactions on Information Forensics and Security, 2019,14(9): 2512-2524. [14] 吴超,邵曦.基于深度学习的指静脉识别研究[J].计算机技术与发展,2018,28(2):200-204. [15] Miura N, Nagasaka A, Miyatake T. Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification[J]. Systems & Computers in Japan, 2004, 35(7):61-71. [16] Miura N, Nagasaka A, Miyatake T. Extraction of finger-vein patte-rns using maximum curvature points in image profiles[J]. IEICE Transactions on Information and Systems, 2007, 90(8): 1185-1194. [17] Song W, Kim T, Kim HC,et al. A finger-vein verification system using mean curvature[J]. Pattern Recognition Letters,2011,32(11):1541-1547. [18] Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971- 987. [19] Sikarwar P. Finger vein recognition using local directional pattern[C]//2016 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2016, 1: 1-5. [20] Meng Xiaojing, Yang Gongpin, Yin Yilong, et al. Finger vein recognition based on local directional code[J]. Sensors, 2012, 12(11): 14937-14952. [21] Rosdi B A, Shing C W, Suandi S A. Finger vein recognition using local line binary pattern[J]. Sensors, 2011, 11(12): 11357-11371. [22] 陈晓腾. 前臂静脉特征的提取与匹配方法研究[D].南京:南京航空航天大学,2020. [23] 陈雅玫. 手掌静脉红外图像识别方法研究[D].重庆:重庆邮电大学,2019. [24] 郑宪秋. 人体手指静脉图像识别算法研究[D].南昌:东华理工大学,2016. [25] 桂青. 多特征点融合的手背静脉身份识别算法研究[D].杭州:浙江大学,2020. [26] 吴超,邵曦. 基于深度学习的指静脉识别研究[J].计算机技术与发展,2018,28(2):200-204. [27] Lu Yu, Xie Shajuan, Yoon S, et al. Robust finger vein ROI localization based on flexible segmentation[J]. Sensors, 2013, 13(11): 14339-14366. [28] Choi JH, Song W, Kim T, et al. Finger vein extraction using gradient normalization and principal curvature[C]//Image Processing: Machine Vision Applications II. San Jose: International Society for Optics and Photonics, 2009:7251. [29] 曹迪. 基于结构特征的手指静脉识别方法研究[D].天津:中国民航大学,2014. [30] 危辉,李俐.拱序列的曲线描述与匹配[J].中国图象图形学报,2017,22(8):1045-1055. [31] 魏鑫. 手指静脉图像识别的技术研究[D]. 北京:北京邮电大学,2019. [32] 卞加祁,姚志均,胡学龙,等.基于LTP和局部PCA的低分辨率人脸识别算法[J].扬州大学学报(自然科学版),2020,23(2):47-51. [33] Wang Kuanquan, Khisa AS, Wu Xiangqian, et al. Finger vein recognition using LBP variance with global matching[C]//2012 International Conference on Wavelet Analysis and Pattern Recognition. Xi′an: IEEE, 2012: 196-201. [34] Yu Chengbo, Qin Huafeng, Cui Yanzhe, et al. Finger-vein image recognition combining modified hausdorff distance with minutiae feature matching[J]. Interdisciplinary Sciences: Computational Life Sciences, 2009, 1(4): 280-289. [35] Liu Zhi, Yin Yilong, Wang Hongjun, et al. Finger vein recognition with manifold learning[J]. Journal of Network and Computer Applications, 2010, 33(3): 275-282. [36] Yang Jinfeng, Zhang Xu. Feature-level fusion of global and local features for finger-vein recognition[C]//IEEE 10th International Conference on Signal Processing Proceedings. Beijing: IEEE, 2010: 1702-1705. [37] 王科俊,刘靖宇,李雪峰. 基于相对距离的手指静脉识别方法[J].计算机科学,2011,38(09):257-259+270. [38] Zhang Hu, Wang Xianliang, He Zhixiang. Finger Vein Recognition via Local Multilayer Ternary Pattern[C]//Chinese Conference on Biometric Recognition. Beijing: Springer, Cham, 2016: 271-278. [39] Hayat NAM, Noh ZM, Yatim NM, et al. Analysis of local binary pattern using uniform bins as palm vein pattern descriptor[J]//Journal of Physics,20220, 1502(1): 012043.
