LightLiveAuth: A Lightweight Continuous Authentication Model for Virtual Reality

As network infrastructure and Internet of Things (IoT) technologies continue to evolve, immersive systems such as virtual reality (VR) are becoming increasingly integrated into interconnected environments. These advancements allow real-time processing of multi-modal data, improving user experiences with rich visual and three-dimensional interactions. However, ensuring continuous user authentication in VR environments remains a significant challenge. To address this issue, an effective user monitoring system is required to track VR users in real time and trigger re-authentication when necessary. Based on this premise, we propose a multi-modal authentication framework that uses eye-tracking data for authentication, named MobileNetV3pro. The framework applies a transfer learning approach by adapting the MobileNetV3Large architecture (pretrained on ImageNet) as a feature extractor. Its pre-trained convolutional layers are used to obtain high-level image representations, while a custom fully connected classification is added to perform binary classification. Authentication performance is evaluated using Equal Error Rate (EER), accuracy, F1-score, model size, and inference time. Experimental results show that eye-based authentication with MobileNetV3pro achieves a lower EER (3.00%) than baseline models, demonstrating its effectiveness in VR environments.