摘要
针对AlexNet在手指静脉识别系统中训练耗时过长,识别准确率较低的问题,提出AlexNet的改进网络结构。针对AlexNet模型输入图像尺寸限制性强,自适应能力差的问题引入空间金字塔池化模式的网络结构。为了加快网络训练速度和降低网络模型的复杂度,对AlexNet的卷积核尺寸、网络深度和全连接层等进行调整。实验结果表明,改进后的网络模型在公开和自有指静脉数据集上的识别准确率及训练时长较AlexNet模型均有明显改善。
An improved AlexNet structure is proposed to solve the problem of long time and low recognition accuracy of an AlexNet training finger vein recognition system.To address the problem of limited image size and poor adaptability of an AlexNet network model,the network structure of spatial pyramid pooling mode is introduced.To fasten the network’s training speed and reduce the complexity of the network model,the convolution kernel size of AlexNet,network depth,and the full connection layer are adjusted.Results show that the improved network model has a significant improvement on the recognition accuracy and training duration compared with the AlexNet model in both public and private finger vein datasets.
作者
陶志勇
胡亚磊
林森
Tao Zhiyong;Hu Yalei;Lin Sen(School of Electronic&Information Engineering,Liaoning Technical University,Huludao,Liaoning 125105,China;Fuxinlixing Technology Company Limited,Fuxin,Liaoning 123000,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2020年第8期50-58,共9页
Laser & Optoelectronics Progress
基金
国家重点研发计划(2018YFB1403303)
辽宁省博士启动基金(20170520098)。
关键词
图像处理
指静脉识别
卷积神经网络
空间金字塔池化
image processing
finger vein recognition
convolutional neural network
spatial pyramid pooling
