Finger Vein Identification Based On Transfer Learning of AlexNet

Subha Fairuz, M. H. Habaebi, E. Elsheikh
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引用次数: 22

Abstract

Nowadays finger vein-based validation systems are getting extra attraction among other authentication systems due to high security in terms of ensuring data confidentiality. This system works by recognizing patterns from finger vein images and these images are captured using a camera based on near-infrared technology. In this research, we focused finger vein identification system by using our own finger vein dataset, we trained it with transfer learning of AlexNet model and verified by test images. We have done three different experiments with the same dataset but different sizes of data. Therefore, we obtained varied predictability with 95% accuracy from the second experiment.
基于AlexNet迁移学习的手指静脉识别
目前,基于手指静脉的验证系统由于在确保数据保密性方面具有较高的安全性,在其他认证系统中越来越受到关注。该系统通过识别手指静脉图像的模式来工作,这些图像是使用基于近红外技术的相机捕获的。在本研究中,我们利用自己的手指静脉数据集对手指静脉识别系统进行了聚焦,利用AlexNet模型的迁移学习对其进行了训练,并通过测试图像进行了验证。我们用相同的数据集做了三个不同的实验,但数据大小不同。因此,我们从第二个实验中获得了95%准确率的可预测性。
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