Mobile ocular biometrics in visible spectrum using local image descriptors: A preliminary study
Ocular biometrics refers to personal identification using iris, conjunctival vasculature, periocular or eye movements. Contrary to most of other biometric traits, ocular biometrics does not require high user cooperation and close capture distance. Biometrics is now adopted ubiquitously as an alternative to passwords on mobile devices. Especially, ocular biometrics in the visible spectrum has attracted a lot of attention owing to the fact that it can be acquired using the regular RGB cameras already available in all mobile devices. The use of local image descriptors (i.e., analysis of microtextural features) for ocular biometrics is gaining more and more popularity because of their compactness, computationally inexpensiveness, excellent performance and flexibility. In this work, we explore the possibility of performing large scale mobile ocular biometric recognition in the visible spectrum using local image descriptors. We design a weighted fusion scheme to combine the information originating from four different local descriptors. The experimental analysis of the devised scheme, on newly collected and publicly available large scale database using three different mobile devices, shows promising results.