A Novel Application of Universal Background Models for Periocular Recognition

Abstract

In recent years the focus of research in the fields of iris and face recognition has turned towards alternative traits to aid in the recognition process under less constrained acquisition scenarios. The present work assesses the potential of the periocular region as an alternative to both iris and face in such conditions. An automatic modeling of SIFT descriptors, using a GMM-based Universal Background Model method, is proposed. This framework is based on the Universal Background Model strategy, first proposed for speaker verification, extrapolated into an image-based application. Such approach allows a tight coupling between individual models and a robust likelihood-ratio decision step. The algorithm was tested on the UBIRIS. v2 and the MobBIO databases and presented state-of-the-art performance for a variety of experimental setups.