Machine Learning approach to dissimilarity computation: Iris matching
Naiara Aginako, José María Martínez‐Otzeta, Igor Rodríguez, Elena Lazkano, Basilio Sierra · 2016
This paper presents a novel approach for iris dissimilarity computation based on Machine Learning paradigms and Computer Vision transformations. Based on the training dataset given by the MICHE II Challenge organizers, a set of classifiers has been constructed and tested, aiming at classifying a single image. The main novelty of this paper remains in the used approach to iris dissimilarity computation: given two iris images, both of them are classified using the same paradigm, obtaining the a posteriori probability for each of the considered class values. Hence, two distributions are obtained, one for each iris image, and the dissimilarity is computed as the distance between these two distributions. Experimental results indicate the appropriateness of this new approach, even though more research and experiments are needed to obtain some improvements and to accelerate the classification process.