A Multimodal Method for Semi-Biometric Information Based User Identification in AR and VR Applications
Han Li, Ke Lyu, Owen Dossett, Xianglong Feng · 2024
Virtual Reality (VR) and Augmented Reality (AR) have witnessed a surge in popularity, revolutionizing various industries and enhancing user experiences. As these technologies continue to evolve, ensuring secure user identification becomes increasingly important. However, existing identification methods often come with vulnerabilities or require costly hardware implementations. To address these challenges, we propose a novel semi-biometric information-based user identification approach leveraging multimodal techniques. By analyzing the user’s viewing patterns and gaze behavior in the runtime, we can accurately identify individuals. This approach offers a promising solution for seamless user identification in VR and AR applications, without compromising on user experience or requiring expensive hardware modifications. To verify the efficiency of our proposed algorithm, we test our algorithm using a public dataset, the result of which shows a high classification accuracy. Additionally, beyond traditional evaluation metrics, we perform a transferability test, demonstrating the adaptability of our solution to new scenarios with robust generality.