Exploring the Potential of Webcam-Based Eye-Tracking for Traditional Eye-Tracking Analysis

Traditional eye-tracking systems can be costly and may pose a barrier to entry for researchers interested in studying gaze behavior. In recent years, there have been significant developments in simulating eye-tracking using webcams. However, little research has explored the use of webcam-based eye-tracking data for traditional eye-tracking analysis. In this paper, we propose a webcam-based eye-tracking system that utilizes an dilated convolutional neural networks to detect point of gaze and calculate a range of analysis indicators, such as duration of first fixation and latency of first fixation. By integrating these indicators, we aim to explore the potential of webcam-based eye-tracking for traditional eye-tracking analysis. This approach could significantly reduce the barrier to entry for researchers in the field of gaze behavior research and open up new avenues for studying gaze behavior.