Multi-Eyes: A Framework for Multi-User Eye-Tracking using Webcameras

The human gaze provides informative cues on human behavior during interactions in multi-user environments. However, capturing this gaze information using traditional eye trackers often requires complex and costly experimental setups. Furthermore, conventional eye-tracking algorithms are catered for single-user scenarios and cannot be used for multi-user environments. We propose Multi-Eyes, a commodity webcam-based solution offering scalability and cost-efficiency while leveraging the advancements in deep learning for capturing multi-user gaze. Multi-Eyes propose a three-step multi-user eye tracking framework that (1) detects gaze subjects, (2) estimates gaze, and (3) maps gaze-to-screen with a scalable, memory, and parameter-efficient disentangled gaze estimation model. We evaluate the gaze estimation model using two publicly available datasets and the framework's utility through a joint-attention case study. Our proposed architecture achieves the lowest gaze error of 4.33, while the case study demonstrates the feasibility of the Multi-Eyes for multi-user interactions and joint attention with comparable results to the state-of-the-art.