# Exploring the Potential of Event Camera Imaging for Advancing Remote Pupil-Tracking Techniques
## 4 Discussion
For a range from minimal to large movements, the detection accuracy was reduced to 69.1% and tracking to 52.7%, emphasizing the tracking challenges with varying motion intensities. A comparison between our proposed event-camera-based method and state-of-the-art RGB-frame-camera-based techniques [22; 23; 24; 25] is also listed in Table 5.  
Figure 6: Confusion matrix for the proposed pupil localization. Figure 7: Failure cases of the proposed event-camera-based pupil tracking method. (**a**) Minimal movements with obscured eye shape, leading to a failure in eye-nose region detection. (**b**) Visible eye shape, but failure in the alignment and tracker-checker components. Despite the algorithm's successes in handling rapid eye movements and achieving real-time capabilities, there are areas for improvement. One notable limitation is the reduced accuracy when dealing with subtle or minimal movements. When there is minimal-to-no head movement, the nature of event cameras, which primarily respond to changes in the scene, might produce sparse events, complicating eye-nose detection. Our research successfully detected pupil movements across a 1 m range using event cameras, as evidenced in the Figure 1, Figure 5, and Figure 6 shown in the manuscript. However, we recognize the resolution limitation of the DAVIS 346 and anticipate improvements with next-generation high definition (HD) event cameras. These situations may lead to partial occlusion or insufficient motion cues, making it challenging for the algorithm to accurately detect and track pupils. Furthermore, the algorithm's performance was dependent on the quality and availability of data in the training dataset. The limited number of available images in the event camera dataset for training the detector and aligner may have influenced the algorithm's performance on certain motion levels. Another consideration is the trade-off between speed and accuracy. While our proposed method achieved remarkable real-time performance at 200 frames per second at a 640 \(\times\) 480 resolution with a 2.0 GHz CPU, there is room for improving the accuracy at the expense of processing speed. For specific applications that require higher precision, optimizations to balance speed and accuracy should be explored. In this study, our primary goal was to assess the feasibility of using event cameras for remote eye tracking, positioning our work as a preliminary exploration in this domain. We acknowledge that a comprehensive comparison with other eye-tracking methods and a more extensive validation were not undertaken. Additionally, to overcome these limitations and further enhance the algorithm's performance, several strategies can be considered. In this research, we emphasized computational efficiency, favoring methods, like SDM, Adaboost, and LBP. However, to further optimize the performance of our algorithm, considering diverse strategies is important. As deep learning continues to develop with new lightweight networks coming out, adapting or customizing these networks specifically for event cameras could be beneficial. By using recent advanced deep neural networks [26, 27], the algorithm could potentially handle subtle movements more effectively and enhance its robustness in challenging scenarios with obscured eye shapes. Additionally, utilizing the graph structure, especially through approaches, like the graph Fourier transform as discussed in ref. [28], can be adopted to improve tracking capabilities, offering a richer representation of data relationships. Moreover, efforts to expand the training dataset with a more diverse range of event camera data could be undertaken. By collecting data from various real-world environments and users, the algorithm can better