## 4 Experimental Results and Comparisons  
In the experimental environment, the general illuminance values measured by the spectrometer are 203.68 lx. To verify the proposed pupil tracking design under a strong light environment, we use a desk lamp to create the effect, and the measurement values of illuminances are 480.22 lx. On the contrary, for verifying the proposed pupil tracking design under a darker environment, the illuminance values of the experimental environment are set to 86.36 lx. In addition to achieving real-time operation effects, the used YOLOv3-tiny-based deep-learning models can solve the problem of light and shadow interferences that cannot be solved effectively by the traditional pupil tracking methods. In our experiments, a computing server with two NVIDIA GPU accelerator cards was used to train the pupil tracking models, and a personal computer with a 3.4 GHz operating frequency CPU and an NVIDIA GPU card was used to test the YOLO-based inference models. The visible-light wearable gaze tracker utilized two camera modules for shooting, one of which is an outward scene camera module with a resolution of 1600 \(\times\) 1200 pixels, and the other is a visible-light nearfield eye camera module, which provides a resolution of 1280 \(\times\) 720 pixels and can reach 30 frames per second. Through the proposed YOLOv3-tiny-based deep-learning methods to track the position of the pupil center, the size of the pupil's detection box is between 20 \(\times\) 20 and 30 \(\times\) 30 pixels; the pupil center at any position of the eyeball can be correctly inferred, and the inference results are shown in Figure 14. Table 3 lists the performance comparison of visible-light pupil object detection among the three YOLO-based models. In Table 3, the three YOLO-based deep-learning-based models perform the pupil detection precision, which is up to 80%, and the recall rate is up to 83%. Figure 14: The inference results by using the YOLOv3-tiny-based model. For the tested eye images, to evaluate the accuracy of the trained YOLOv3-tiny-based models, Table 4 lists the comparison results of visible-light pupils' center tracking errors between the YOLOv3-tiny and the previous designs without using deep-learning technologies. In Table 4, the applied YOLOv3-tiny-based design performs smaller pupil tracking errors than the previous designs in [15,21]. The average pupil tracking error of the proposed deep-learning-based model is less than 2 pixels, which is much smaller than the pupil tracking error of the previous ellipse fitting design in [15]. Compared with the previous design in [15,21], without using deep-learning technology, the proposed deep-learning-based design has very small pupil tracking errors under various visible-light conditions. To verify the pupil tracking errors of the different YOLOv3-tiny-based deep-learning models, Table 5 lists the comparison results of the visible-light pupils' center tracking errors among the three YOLO-based models, where the datasets of person 1 to person 8 are used for the training mode. In Table 5, the average pupil tracking errors of the three YOLOv3-tiny-based deep-learning-based models are less than 2 pixels. Table 6 lists the comparison results of the visible-light pupils' center tracking errors among the three YOLO-based models for the cross-person testing mode. In Table 6, the average pupil tracking errors of the three YOLOv3-tiny-based deep-learning-based models are less than 5 pixels. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Ellipse Fitting Based** & **Gradients-Based Design** & **YOLOv3-Tiny** \\  & **Design**[15] & **[21] & **(Original)** \\ \hline Average tracking errors & & & \\ (pixels) & Larger than 50 & 5.99 & 1.62 \\ \hline Standard deviation & & & \\ (pixels) & 63.1 & N/A & 2.03 \\ \hline Max/min errors & & & \\ (pixels) & 294/0 & 40/0 & 21/0 \\ \hline \hline \end{tabular}
\end{table}