# Exploring the Potential of Event Camera Imaging for Advancing Remote Pupil-Tracking Techniques
## 3 Experimental Results  
To evaluate the performance of event camera-based pupil tracking, we conducted comprehensive experiments using a diverse dataset that includes various eye movement scenarios and lighting conditions. We compared the results obtained from our proposed event-camera-based method with our previous frame-based eye-tracking algorithms [11]. The experimental results convincingly showcased the potential of event camera imaging in significantly enhancing the accuracy and robustness of pupil tracking, especially during rapid eye movements. Our proposed method was implemented using C++ and tested solely based on CPU computations on a Windows PC. Remarkably, the algorithms achieved an impressive speed of 200 frames per second at a 640 \(\times\) 480 resolution with a 2.0 GHz CPU, showcasing their real-time capabilities and practical suitability for various applications. Table 1 summarizes the pupil-tracking specification. The eye-nose detection uses a cascaded Adaboost classifier combined with multiblock LBP, incorporating nine boosting substages. For pupil localization, the aligner adopts an SDM-based 11-point eye-nose-alignment technique that integrates SIFT features through a 4-step regression. These predefined 11 points include the left eye's outer and inner corners, its center; the right eye's outer and inner corners, its center; and the nasion, pronasale, left alare, subnasale, and right alare. We assessed the precision of our algorithm by computing the disparity between the ground truth and the tracked pupil centers. This method provides a direct quantitative measure of the algorithm's performance by identifying how closely our tracking aligns with known truth values. To relate these disparities to real-world measurements, we utilized the inter-pupil distance (IPD) as a reference. Assuming an IPD of 65 mm, which is a general average for adults, we converted pixel distances into physical distances, enabling us to accurately estimate the positions of the pupils in real-world units. Consequently, our event-camera-based pupil-tracking method achieved a detection accuracy of 98.1% and a tracking accuracy (pupil precision < 10 mm) of 80.9%. The detection accuracy was on par with existing RGB-frame-camera-based pupil-tracking methods, while the tracking accuracy, though slightly lower than frame-camera-based algorithms, proved reasonable and promising for an initial exploration of event-camera-based pupil tracking. A summary of these results is provided in Table 2.  
The dataset employed for our proposed method was thoughtfully constructed, consisting of real-world videos captured using the DAVIS 346 event camera. To train the eye-nose region detector, we used a combination of images from both the subtle and large motion categories of the event camera dataset. We efficiently detected eye-nose regions in the event camera images using the EBL method [11]. Due to the limited number of available images in the dataset, our focus was on the early and middle stages of the EBL training process, excluding the final matured stage. Specifically, the detector training incorporated 3608 event camera images along with 3949 negative non-face background images. For the eye-nose region aligner, we exclusively concentrated on the large motion category of the event camera dataset during training. The aligner's primary task was to accurately handle eye shapes under significant motion, making the large motion DB particularly well-suited for this purpose. The event camera alignment training comprised 2273 events with verifiable eye shape. By constructing and utilizing this specialized event camera dataset, we ensured the algorithms' adaptability to challenging real-world scenarios involving various eye movements and head poses. The proposed algorithm was evaluated based on a video DB captured in a normal office environment with illumination ranging from 100 to 400 lux, using the DAVIS346 event camera. The DB consisted of videos categorized into three distinct motion levels, each having a verifiable eye shape.