Periocular Recognition under Unconstrained Settings with Universal Background Models

The rising challenges in the fields of iris and face recognition are leading to a renewed interest in the area. In recent years the focus of research has turned towards alternative traits to aid in the recognition process under less constrained image acquisition conditions. The present work assesses the potential of the periocular region as an alternative to both iris and face in such scenarios. An automatic modeling of SIFT descriptors, regardless of the number of detected keypoints and 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.