2018 24th International Conference on Pattern Recognition (ICPR)
Download PDF

Abstract

Finger vein pattern has been proven to be an effective biometric for personal identification in recent years. Nevertheless, there remain challenges that need to be solved, such as finger-vein features that lack robustness and expressiveness. In this paper, we propose a deep convolutional neural network (CNN) model, named the Finger-vein Network (FV-Net), to learn the features representative of a finger vein that is more discriminative and robust than handcrafted features. Next, to address the issue of translation and rotation in vein imaging, we propose a template-like matching strategy while designing the top architecture of the FV-net to extract features with spatial information. Finally, the extensive experimental results show that our proposed method can achieve excellent performance on several public datasets.
Like what you’re reading?
Already a member?Sign In
Member Price
$11
Non-Member Price
$21
Add to CartSign In
Get this article FREE with a new membership!

Related Articles