A Fusion Method Based on Decision Reliability Ratio for Finger Vein Verification

TL;DR

This paper introduces a novel fusion method for finger vein verification that uses decision reliability ratios to improve accuracy, outperforming traditional fusion techniques.

Contribution

It is the first to define decision reliability ratio for confidence measurement and incorporate it into a fusion method for finger vein verification.

Findings

MDRR achieves 99.42% accuracy, surpassing individual classifiers.

MDRR outperforms traditional fusion methods like Voting and Sum.

Experiment on 1000 fingers demonstrates effectiveness.

Abstract

Finger vein verification has developed a lot since its first proposal, but there is still not a perfect algorithm. It is proved that algorithms with the same overall accuracy may have different misclassified patterns. We could make use of this complementation to fuse individual algorithms together for more precise result. According to our observation, algorithm has different confidence on its decisions but it is seldom considered in fusion methods. Our work is first to define decision reliability ratio to quantify this confidence, and then propose the Maximum Decision Reliability Ratio (MDRR) fusion method incorporating Weighted Voting. Experiment conducted on a data set of 1000 fingers and 5 images per finger proves the effectiveness of the method. The classifier obtained by MDRR method gets an accuracy of 99.42% while the maximum accuracy of the original individual classifiers is 97.77%. The experiment results also show the MDRR outperforms the traditional fusion methods as Voting, Weighted Voting, Sum and Weighted Sum.