摘要
提出基于Hessian矩阵特征值比率的增强算法和改进的Gabor滤波特征提取算法相结合的手指静脉特征提取方法。将不同尺度下的Hessian矩阵的特征值应用于静脉增强函数中进行图像增强,然后再通过Gabor滤波进行特征提取,并在Poly U手指静脉数据库对所提算法进行实验验证。结果表明,该算法用于身份识别时的误识率为0. 375%,拒识率为1. 73%,等误率为0. 021%,比目前其他的手指静脉提取算法的识别性能有所提高。
This paper proposed a finger- vein feature extraction method combining an enhancement algorithm based on the ratio of Hessian matrix eigenvalues (ROE)and a feature extraction algorithm based on the improved Gabor filtering. The Hessian matrix eigenvalues at different scales were applied to the vein enhancement function to perform image enhancement and then the feature extraction was conducted through Gabor filters, and the algorithm was validated in the finger vein database of the PolyU. The experimental results show that the algorithm for identification has a false acceptance rate of 0.375%, false rejection rate of 1.73% and the equivalent error rate is 0.021%, which improves the recognition performance of other finger vein extraction algorithms.
作者
杨如民
许琳英
余成波
YANG Rumin;XU Linying;YU Chengbo(College of Electrical and Electronic Engineering, Chongqing University of Techology, Chongqing 400054, China)
出处
《兵器装备工程学报》
CAS
北大核心
2019年第3期103-107,111,共6页
Journal of Ordnance Equipment Engineering
基金
国家自然科学基金项目(61402063)
