A Decision Reliability Ratio Based Fusion Scheme for Biometric Verification

Unimodal biometric verification has developed a lot and become more accurate, but there is still not a perfect algorithm. In the meantime, cases exist where unimodal verification system could not meet the requirements in practical use. 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 this is seldom considered in fusion methods. Our work focuses on this confidence. We first define decision reliability ratio to quantify this confidence, and then propose the Maximum Decision Reliability Ratio (MDRR) fusion scheme incorporating Weighted Voting. Two experiments conducted on different datasets prove the effectiveness of the method. One is to fuse 4 finger vein verification algorithms on a set of 1000 fingers and 5 images per finger. The other experiment fuse the multimodal set in NIST-BSSR1. Experiment results show the fusion method could largely improve verification accuracy, from 91.29% to 99.81%. It also shows the MDRR outperforms the commonly used fusion methods as Voting, Weighted Voting, Weighted Sum or even the theoretically optimal method Likelihood Ratio-based fusion.