Weighted mean assignment of a pair of correspondences using optimisation functions

Abstract

Consensus strategies have been recently studied to help machine learning ensure better results. Likewise, optimisation in graph matching has been explored to accelerate and improve pattern recognition systems. In this paper, we present a fast and simple consensus method which, given two correspondences of sets generated by separate entities, enounces a final consensus correspondence. It is based on an optimisation method that minimises the cost of the correspondence while forcing it (to the most) to be a weighted mean. We tested our strategy comparing ourselves with the classical minimum cost matching system, using a palmprint database, with each palmprint is represented by an average of 1000 minutiae. © 2014 Springer-Verlag Berlin Heidelberg.