ResNet多输出特征的手指静脉识别  

Finger Vein Recognition Based on ResNet Multi Output Feature

作　　者：凌旭东 李朝荣[1,2] 廖天星 魏康林 LING Xudong;LI Chaorong;LIAO Tianxing;WEI Kanglin(Department of Artificial Intelligence and Big Data,Yibin University,Yibin,Sichuan 644000,China;School of Computer Science,Chongqing University of Technology,Chongqing 400054,China;Intelligent Manufacturing Institute,Yibin University,Yibin,Sichuan 644000,China)

机构地区：[1]宜宾学院人工智能与大数据学部,四川宜宾644000 [2]重庆理工大学计算机学院,重庆400054 [3]宜宾学院智能制造学部,四川宜宾644000

出　　处：《宜宾学院学报》2023年第6期11-16,71,共7页Journal of Yibin University

基　　金：中央引导地方专项发展基金项目“黎曼流形深度学习研究及其应用”(2021ZYD0020);宜宾学院科研项目(0219024502,2021YY06);宜宾学院教改项目(JGZ202117)。

摘　　要：为了解决成像环境复杂、生物组织引起红外光散射导致识别准确率低的问题,在基于神经网络分类的手指静脉识别技术基础上,提出神经网络拓展结构模型,并采用机器学习方法计算区分性好的特征,弥补原神经网络引起的部分特征缺失,避免了模型迁移到小型数据库上由于样本数量不足带来的性能下降问题.利用ResNet18、ResNet34和ResNet50三种神经网络在手指静脉数据库上进行的分析与实验表明,拓展结构模型显著提高了识别性能.In order to solve the problem of complex imaging environment and low recognition accuracy caused by infrared light scattering resulting in biological tissues,an extended structure model of neural network was proposed on the basis of finger vein recognition technology based on neural network classification,and machine learning method was used to calculate features with good discrimination for making up for part of the missing features caused by the original neural network.This avoids the perfor⁃mance degradation caused by model migration to a small database due to insufficient sample size.The analysis and experiment on finger vein database using ResNet18,ResNet34 and ResNet50 neural networks show that the extended structure model signifi⁃cantly improves the recognition performance.

关 键 词：生物特征识别 手指静脉 欧氏距离 Canny边缘检测器 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]