沈阳大学学报（自然科学版）2023，Vol.35Issue(6)：511-520,10.
融合注意力机制和残差网络的掌纹识别
Palmprint Recognition Integrating Attention Mechanism and Residual Network
摘要
Abstract
In order to improve the accuracy of the palmprint recognition algorithm and solve the problem of low utilization of palmprint texture information,an improved residual network(ECA-MNet)based on efficient channel attention mechanism was proposed to classify palmprint images.The residual module was improved on the basis of the original residual network.Multiple improved residual modules were spliced,and efficient channel attention mechanism modules were added inside the residual modules to highlight key features through weight allocation.The experimental results showed that the recognition accuracy of ECA-MNet on the four public palmprint datasets was improved compared with other classical network models,and the recognition accuracy on the self-built palmprint datasets reached 98.21％.关键词
掌纹识别/深度学习/图像处理/注意力机制/残差网络Key words
palmprint recognition/deep learning/image processing/attention mechanism/residual network(ResNet)分类
信息技术与安全科学引用本文复制引用
于霞,付琪,薛丹,王健行,武家逸,赵鑫峰..融合注意力机制和残差网络的掌纹识别[J].沈阳大学学报（自然科学版）,2023,35(6):511-520,10.基金项目
国家自然科学基金资助项目(62301339) （62301339）
辽宁省教育厅高等学校基本科研资助项目(LJKMZ20220478). （LJKMZ20220478）
