  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.
