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

• URL: http://arxiv.org/abs/2010.11700v1
• Date: Tue, 20 Oct 2020 17:05:11 GMT
• Title: On Benchmarking Iris Recognition within a Head-mounted Display for AR/VR Application
• Authors: Fadi Boutros, Naser Damer, Kiran Raja, Raghavendra Ramachandra, Florian Kirchbuchner and Arjan Kuijper
• Abstract summary: We evaluate a set of iris recognition algorithms suitable for Head-Mounted Displays (HMD) We employ and adapt a recently developed miniature segmentation model (EyeMMS) for segmenting the iris. Motivated by the performance of iris recognition, we also propose the continuous authentication of users in a non-collaborative capture setting in HMD.
• Score: 16.382021536377437
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Augmented and virtual reality is being deployed in different fields of applications. Such applications might involve accessing or processing critical and sensitive information, which requires strict and continuous access control. Given that Head-Mounted Displays (HMD) developed for such applications commonly contains internal cameras for gaze tracking purposes, we evaluate the suitability of such setup for verifying the users through iris recognition. In this work, we first evaluate a set of iris recognition algorithms suitable for HMD devices by investigating three well-established handcrafted feature extraction approaches, and to complement it, we also present the analysis using four deep learning models. While taking into consideration the minimalistic hardware requirements of stand-alone HMD, we employ and adapt a recently developed miniature segmentation model (EyeMMS) for segmenting the iris. Further, to account for non-ideal and non-collaborative capture of iris, we define a new iris quality metric that we termed as Iris Mask Ratio (IMR) to quantify the iris recognition performance. Motivated by the performance of iris recognition, we also propose the continuous authentication of users in a non-collaborative capture setting in HMD. Through the experiments on a publicly available OpenEDS dataset, we show that performance with EER = 5% can be achieved using deep learning methods in a general setting, along with high accuracy for continuous user authentication.

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