# Exploring the Potential of Event Camera Imaging for Advancing Remote Pupil-Tracking Techniques
## 2 Proposed Method
### Pupil-Tracking Algorithm
While CNNs exhibit remarkable performance in computer vision tasks, they often demand significant computational resources and result in longer processing times, making them less suitable for real-time applications. In contrast, our chosen approach, which utilized a multi-block LBP-based detector and SDM-based aligner, exhibited faster and more efficient performance, particularly on conventional CPUs in PCs or mobile tablets with limited graphic processing unit (GPU) resources [4; 11]. The LBP-based detector enabled straightforward and practical eye-nose region detection, while the SDM-based aligner ensured accurate and real-time eye center position tracking. This capability was essential for capturing rapid eye movements during pupil-tracking tasks. Our previous studies [4; 11] highlighted that the SDM-based eye tracking achieved an impressive speed of 4 ms per 640 by 480 image resolution with CPU usage. In comparison, various CNN-based algorithms, such as ESR (15 ms, CPU) [20], DVLN (15 ms, CPU) [21], and LAB (2.6 s, CPU) [22], required significantly more time under the same conditions. The superior speed and efficiency of our method make it highly suitable for real-time eye-tracking applications, especially when dealing with rapid eye movements, dynamic environments, and limited computing resources. In the context of event camera imaging, we have adapted and applied our previously developed machine-learning-based computer vision algorithm for pupil tracking. Our new algorithm is specifically designed to work with event camera data and includes feature extraction, eye-nose detection, and alignment methods. By combining insights from frame-based eye-tracking research with our knowledge from bare-face eye tracking, our goal is to effectively utilize the unique capabilities of event camera imaging. The main components of our tracking system are divided into three stages: (1) eye-nose region detection from event camera images, which accumulate asynchronous events during a fixed time, (2) tracking  
Figure 4: Flowchart of the proposed pupil-tracking method based on event camera imaging. This visual representation details the process, starting from event frame generation, moving to eye–nose detection and alignment, and ending with the tracking checker. the eye center position based on the detected eye-nose region, and (3) a tracker-checker for fast tracking. For eye-nose region detection, we employ the error-based learning (EBL) method [11], using cascaded Adaboost classifiers with multiblock LBP. This approach is designed to make optimal use of standard CPUs found in personal computers (PCs) or mobile tablets with limited GPU resources. Upon successful eye-nose region detection, the eye center position tracking mode is activated. We use a coarse-to-fine strategy to infer the pupil center location through the use of the SDM with SIFT features, followed by pupil position refinement through the pupil-segmentation module. The SDM-based shape alignments rely on 11 landmark points that cover the eyes and nose areas. To enhance pupil-tracking performance using event cameras, we developed a specialized event camera image database (DB) and implemented efficient learning methods. The DB was created by capturing real-world videos using the DAVIS 346 event camera, and the videos were categorized into three motion levels as shown in Figure 1: minimal motion, subtle motion, and large motion with verifiable eye shape. For training the eye-nose region detector, we utilized images from both the subtle and large motion categories. We employed the EBL method to efficiently detect eye-nose regions from the event camera images. Typically, the EBL method, which is inherently iterative, progresses through three stages refining and reducing large datasets to essential samples. Due to the limited number of images available in the DB, we focused on the early and middle stages of the EBL training process, leaving out the final matured stage [11]. As for the eye-nose region aligner, we concentrated solely on the large motion category for training.