RANK FUSION METHOD

The rank level fusion approach is used in biometric identification systems when the individual matcher’s output is a ranking of the “candidates” in the template database sorted in a decreasing order of match scores (or, an increasing order of distance score in appropriate cases). The system is expected to assign a higher rank to a template that is more similar to the query. Plurality voting method, highest rank method, Borda count method, logistic regression method, Bayesian method and quality based method are reported in the literature to perform rank level fusion in multi biometric system. All of these biometric rank fusion approaches are discussed in the following subsections.

The plurality voting method is a positional method for rank aggregation which takes into account information about individuals' preference orderings. However, this method does not take into account a matcher's entire preference ordering, instead uses only information about each voter's most preferred alternative. This method is good for combining a small number of specialized matchers. In this method, the consensus ranking is obtained by sorting the identities according to their number of position in the top position.

The highest rank method is good for combining a small number of specialized matchers and hence can be effectively used for a multimodal biometric system where the individual matchers perform well. In this method, the consensus ranking is obtained by sorting the identities according to their highest rank.

The steps in Algorithm 2 show the procedure of employing highest rank fusion method in a multimodal biometric system.