基于曲线描述子的手指静脉识别  

Finger Vein Recognition Based on Curve Descriptor

作　　者：苏丹 王新强[1] 刘宇航[2] 陆瑶芃 李婷 聂泽东[2] Su Dan;Wang Xinqiang;Liu Yuhang;Lu Yaopeng;Li Ting;Nie Zedong(School of Electronic Engineering and Automation,Guilin University of Electronic Technology,Guilin 541004,China;Shenzhen Institute of Advanced Technology,Chinese Academy of Science,Shenzhen 518055,Guangdong,China)

机构地区：[1]桂林电子科技大学电子工程与自动化学院,桂林541004 [2]中国科学院深圳先进技术研究院,广东深圳518055

出　　处：《中国生物医学工程学报》2022年第4期420-430,共11页Chinese Journal of Biomedical Engineering

基　　金：国家重点研发计划资助项目(2018YFC2001002);国家自然科学基金(62173318);深圳市基础研究资助项目(JCYJ20180507182231907)。

摘　　要：手指静脉识别因为具有高防伪性、唯一性、稳定性和活体检测等优点,成为身份识别领域的研究热点。目前大多数基于指静脉结构特征的识别算法仅考虑到了细节点特征,却忽略了静脉网络结构的曲线特征,造成一部分结构信息的丢失,影响识别结果。针对上述问题,提出一种基于曲线描述子的手指静脉识别算法。首先,提取出指静脉的骨架结构,检测静脉交叉点和端点,并利用交叉点和端点将静脉骨架分割为若干条曲线段;其次,通过交叉点和曲线段的相对位置及形状特征提出曲线弧描述子和交叉弧描述子,并提取指静脉的结构特征矩阵;最后,根据提出的加权距离式计算匹配交叉弧对进行图像匹配。对实验室采集的来自56名志愿者的840张手指静脉图像进行算法实验,结果表明,传统的局部二值模式(LBP)、局部三值模式(LTP)和加速稳健特征(SURF)算法的等错误率分别为4.47%、3.99%和6.08%,而本方法的等错误率仅为1.63%。所提出方法在指静脉识别中具有一定的普适性和应用前景。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.

关 键 词：曲线描述子 交叉点 端点 弧描述子 交叉弧描述子 

分 类 号：R318[医药卫生—生物医学工程]