Verification technology for finger vein biometric

TL;DR

This paper presents a finger vein verification system that enhances image quality using CLAHE, compares pretrained CNN models VGG16 and ResNet50 across multiple datasets, and evaluates their effectiveness for biometric identification.

Contribution

It introduces a verification system utilizing CLAHE and fine-tuned CNN models, demonstrating improved accuracy and robustness in finger vein biometric identification.

Findings

CLAHE improves image contrast and detail detection.

ResNet50 outperforms VGG16 in accuracy.

System tested successfully on multiple datasets.

Abstract

Finger vein biometrics is an approach to identifying individuals based on the unique patterns of blood vessels in their fingers, and the technology is advanced in image capture and processing techniques, which is leading to more efficient, accurate, and reliable systems. This article focuses on a verification system that compares the matrices of an efficient finger vein verification system on different databases to test its strength and efficiency. Contrast Limited Adaptive Histogram Equalization (CLAHE) has been examined as an image enhancement and processing method to improve contrast and render details in an image easier to detect. A random forest classifier is deployed with a comparison between two pretrained systems, VGG16 and ResNet50, which are types of convolutional neural networks. VGG-16 and ResNet-50 models are implemented on three different datasets, and fine-tuning these models enabled the harnessing of their powerful capabilities and achieving superior performance on the specific image classification task.