Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors.

Hyung Hong,Min Lee,Kang Park

doi:10.3390/s17061297

Abstract

Conventional finger-vein recognition systems perform recognition based on the finger-vein lines extracted from the input images or image enhancement, and texture feature extraction from the finger-vein images. In these cases, however, the inaccurate detection of finger-vein lines lowers the recognition accuracy. In the case of texture feature extraction, the developer must experimentally decide on a form of the optimal filter for extraction considering the characteristics of the image database. To address this problem, this research proposes a finger-vein recognition method that is robust to various database types and environmental changes based on the convolutional neural network (CNN). In the experiments using the two finger-vein databases constructed in this research and the SDUMLA-HMT finger-vein database, which is an open database, the method proposed in this research showed a better performance compared to the conventional methods.

Highlights

Typical biometric technologies include face [1,2], fingerprint [3,4], iris [5,6], and finger-vein recognition [7,8]
We propose a finger-vein recognition method that is robust to various database types and environmental changes based on the convolutional neural network (CNN)
In the case of texture feature extraction, the developer must experimentally decide on a form of the optimal filter for extraction considering the characteristics of the image database

Summary

Introduction

Typical biometric technologies include face [1,2], fingerprint [3,4], iris [5,6], and finger-vein recognition [7,8]. In previous research [11], the performance comparisons of biometric technologies of face, fingerprint, iris, finger-vein, and voice are explained. There are generally two factors that lower the recognition performance in finger-vein recognition systems: misalignment by translation and rotation of the finger, which occurs at the time of capturing the finger-vein image, and shading on the finger-vein image. The first factor involves the misalignment between the finger-vein patterns in the enrolled image and the recognition image due to the translation and rotation of the finger on the finger-vein image capturing device during a recognition attempt

Methods

Results

Conclusion