LightLiveAuth: A Lightweight Continuous Authentication Model for Virtual Reality

As network infrastructure and IoT technologies continue to evolve, immersive systems such as virtual reality (VR) are becoming increasingly integrated into interconnected environments. These advancements enable the transmission and processing of vast amounts of multimodal data in real time, enhancing user experiences through rich visual and 3D interactions. However, ensuring continuous user authentication in VR environments remains a significant challenge. Therefore, an effective user monitoring system is needed to track VR users in real time and trigger re-authentication when necessary. Based on this premise, we propose a multi-model authentication framework that combines eye-tracking data and biometric information named Mobilenetv3pro. Using the MobileNetV3 model, we extract and classify eye region features, while an CNN-based model processes sequential behavioral data. Authentication performance is measured through Equal Error Rate (EER), accuracy, Recall, F1-score, model size and inference time. Experimental results show that eye-based authentication using MobileNetV3pro achieves a lower EER (0.03) compared to baseline models, demonstrating its effectiveness in VR environments.