Biometric Person Authentication IS A Multiple Classifier Problem
Abstract
Several papers have already shown the interest of using multiple classifiers
in order to enhance the performance of biometric person authentication systems.
In this paper, we would like to argue that the core task of Biometric
Person Authentication is actually a multiple classifier problem as such:
indeed, in order to reach state-of-the-art performance, we argue that
all current systems , in one way or another, try to solve several tasks
simultaneously and that without such joint training (or sharing),
they would not succeed as well.
We explain hereafter this perspective, and according to it, we propose
some ways to take advantage of it, ranging from more parameter
sharing to similarity learning.