LAMDA: Label Agnostic Mixup for Domain Adaptation in Iris Recognition

Iris Recognition (IR) is one of the most effective biometric authentication techniques available today. It is an obvious candidate for authentication on most head mounted devices. Networks for IR trained on datasets collected by existing hardware may not generalize to newer hardware due to domain gap induced by changes in sensor configurations noise, resolution, camera placements etc. Coupled with the challenge of acquiring high quality iris samples, domain adaptation in IR is an important topic that remains poorly studied in literature. We introduce the problem of supervised Domain Adaptation (DA) for IR, where we assume access to abundant source training data, but extremely limited labeled target training data. Additionally, we propose a novel mixup strategy called LAMDA that mitigates the domain gap between source and target IR datasets by augmenting samples from these datasets. Unlike existing mixup techniques, LAMDA does not require performing label-mixup, and outperforms existing DA techniques in almost all of our problem settings, irrespective of the availability of target training data, and across various image quality degradations. As a way to facilitate research, we also introduce new dataset splits for the problem.