# Exploring the Potential of Event Camera Imaging for Advancing Remote Pupil-Tracking Techniques
## 4 Discussion  
In this study, the primary objective was assessing how existing algorithms perform when applied to event camera imaging. Our approach used machine-learning-based computer vision methods, including a multi-block LBP-based detector and SDM-based aligner, to achieve real-time and efficient pupil-tracking performance. For our experiments, we aggregated events into frames at 30 fps to accurately represent eye movements. This rate was chosen for its visual clarity and its compatibility with our SDM-based keypoint alignment. Our algorithm, utilizing the SDM, showed optimal performance with data aggregated at this rate. We evaluated the algorithm's capabilities using a comprehensive dataset that included various eye movement scenarios, and we compared the results with our previous frame-based eye-tracking algorithms. While our event camera-based approach achieved a nearly equivalent detection accuracy of 98.1%, closely matching the 99.4% achieved by traditional RGB frame camera methods [11], there was a noticeable decline in the tracking accuracy. The tracking accuracy dropped from 99.4% with the RGB-frame-camera-based method [11] to 80.9% in our proposed approach. To further quantify our results, we computed the precision and recall for our method. The precision achieved was 100%, and the recall was 80.89%. Based on our testing, the confusion matrix related to our approach has been detailed in Figure 6. Our true negative value was zero, consistent with our test dataset that did not contain any images without pupils. A notable point from our results is the zero false positives. This can be attributed to the intrinsic nature of event cameras, which do not output any pixel value in static backgrounds. Hence, there is no activation unless there is motion. This characteristic eliminates the chances of false positive detections in areas with no movement, showing one of the significant advantages of utilizing event cameras. Since event cameras might not consistently provide uniform shape information, SDM can sometimes fail in its regression tasks, leading to less precise tracking. Moreover, our tracker checker tends to discard tracking instances when the shape information is unclear, especially during non-insufficient movements. Our choice of the AdaBoost-based eye-nose detection combined with the SDM-based pupil alignment was intentionally selected. This conventional method was chosen driven by our primary aim: to examine the feasibility of well-established remote eye-tracking algorithms on rapidly functioning event cameras. As demonstrated in our previous research on RGB-frame-based cameras [11], while advanced deep learning methods may offer improved results, our technique can operate efficiently on CPUs, eliminating the necessity for high-priced GPUs. However, methods, like AdaBoost and SDM, have limitations when confronting outliers, noise, occlusions, and other challenging scenarios compared to newer deep learning techniques. A further study focusing on deep neural networks designed for event cameras is required. Figure 7 showcases some failure cases of the proposed method. In Figure 7a, we observe a scenario with minimal movements and an obscured eye shape, leading to the detector's failure in detecting the eye-nose region. Similarly, Figure 7b presents a case where the eye shape is visible, but the algorithm encounters challenges in the alignment and tracker-checker components, resulting in tracking failure. These instances illustrate the complexities and limitations associated with event camera imaging, particularly when dealing with scenarios involving minimal movements and obscured eye shapes. Additionally, Table 4 summarizes our algorithm's performance across movement levels. In the large movement category, we achieved a detection accuracy of 98% and a tracking accuracy of 80.9%, illustrating our system's ability to track pupils during quick eye motions.