Neuromorphic eye tracking: a survey

Event-based eye tracking represents an innovative approach to analyzing eye movements, leveraging event cameras’ high temporal resolution and asynchronous nature. This paper provides a comprehensive field review, focusing on datasets, algorithms, and challenges. It examines key datasets, highlighting their diversity and limitations, and categorizes algorithms into two core approaches: frame-based and spiking neural network (SNN)-based approaches. Frame-based methods leverage traditional techniques and deep learning, while SNNs represent an emerging field offering biologically inspired, energy-efficient solutions. However, the computational demands of emerging deep-learning methods raise questions about their feasibility for deployment on consumer-grade devices, particularly embedded systems like smart eyewear. This review provides a structured analysis of current advancements, datasets, and challenges, offering insights to guide the development of efficient and deployable event-based eye-tracking systems.