Periocular Biometrics in Head-Mounted Displays: A Sample Selection Approach for Better Recognition
Virtual and augmented reality technologies are increasingly used in a wide range of applications. Such technologies employ a Head Mounted Display (HMD) that typically includes an eye-facing camera and is used for eye tracking. As some of these applications require accessing or transmitting highly sensitive private information, a trusted verification of the operator's identity is needed. We investigate the use of HMD-setup to perform verification of operator using periocular region captured from inbuilt camera. However, the uncontrolled nature of the periocular capture within the HMD results in images with a high variation in relative eye location and eye opening due to varied interactions. Therefore, we propose a new normalization scheme to align the ocular images and then, a new reference sample selection protocol to achieve higher verification accuracy. The applicability of our proposed scheme is exemplified using two handcrafted feature extraction methods and two deep learning strategies. We conclude by stating the feasibility of such a verification approach despite the uncontrolled nature of the captured ocular images, especially when proper alignment and sample selection strategy is employed.