EFI-SATL:An Efficient Net and Self-Attention Based Biometric Recognition for Finger-Vein Using Deep Transfer Learning  

作　　者：Manjit Singh Sunil Kumar Singla 

机构地区：[1]Department of Electrical and Instrumentation Engineering,Thapar Institute of Engineering and Technology,Patiala,147004,India

出　　处：《Computer Modeling in Engineering & Sciences》2025年第3期3003-3029,共27页工程与科学中的计算机建模(英文)

摘　　要：Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training data.Considering the concerns of existing methods,in this work,a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention mechanism.Data augmentation using various geometrical methods is employed to address the problem of training data shortage required for a deep learning model.The proposed model is tested using K-fold cross-validation on three publicly available datasets:HKPU,FVUSM,and SDUMLA.Also,the developed network is compared with other modern deep nets to check its effectiveness.In addition,a comparison of the proposed method with other existing Finger vein recognition(FVR)methods is also done.The experimental results exhibited superior recognition accuracy of the proposed method compared to other existing methods.In addition,the developed method proves to be more effective and less sophisticated at extracting robust features.The proposed EffAttenNet achieves an accuracy of 98.14%on HKPU,99.03%on FVUSM,and 99.50%on SDUMLA databases.

关 键 词：Biometrics finger-vein recognition(FVR) deep net self-attention Efficient Nets transfer learning 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]