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Augmented and virtual reality is being deployed in different fields ofapplications. Such applications might involve accessing or processing criticaland sensitive information, which requires strict and continuous access control.Given that Head-Mounted Displays (HMD) developed for such applications commonlycontains internal cameras for gaze tracking purposes, we evaluate thesuitability of such setup for verifying the users through iris recognition. Inthis work, we first evaluate a set of iris recognition algorithms suitable forHMD devices by investigating three well-established handcrafted featureextraction approaches, and to complement it, we also present the analysis usingfour deep learning models. While taking into consideration the minimalistichardware requirements of stand-alone HMD, we employ and adapt a recentlydeveloped miniature segmentation model (EyeMMS) for segmenting the iris.Further, to account for non-ideal and non-collaborative capture of iris, wedefine a new iris quality metric that we termed as Iris Mask Ratio (IMR) toquantify the iris recognition performance. Motivated by the performance of irisrecognition, we also propose the continuous authentication of users in anon-collaborative capture setting in HMD. Through the experiments on a publiclyavailable OpenEDS dataset, we show that performance with EER = 5% can beachieved using deep learning methods in a general setting, along with highaccuracy for continuous user authentication.

On Benchmarking Iris Recognition within a Head-mounted Display for AR/VR Application