## 1 Introduction
### Related Works
In [36], the authors developed a low-cost and accurate remote eye-tracking device which used an industrial prototype smartphone with integrated infrared illumination and camera. The proposed design used a 3D gaze-estimation model which enabled accurate point-of-gaze (PoG) estimation with free head and device motion. To accurately determine the input eye features, the design applied the convolutional neural networks with a novel center-of-mass output layer. The hybrid method, which used artificial illumination, a 3D gaze-estimation model, and a CNN feature extractor, achieved better accuracy than the existing eye-tracking methods on smartphones. On the other hand, for the deep-learning-based wearable eye tracking designs, in [28], the eye tracking method with video-oculography (VOG) images needed to provide an accurate localization of the pupil with artifacts and under naturalistic low-light conditions. The authors proposed a fully convolutional neural network (FCNN) for the segmentation of the pupil area that was trained on 3946 hand-annotated VOG images. The FCNN output simultaneously performed pupil center localization, elliptical contour estimation, and blink detection with a single network on commercial workstations with GPU acceleration. Compared with existing methods, the developed FCNN-based pupil segmentation design was accurate and robust for new VOG datasets. In [34], to conquer the obstructions and noises in nearfield eye images, the nearfield visible-light eye tracker design utilized the deep-learning-based gaze tracking technologies to solve the problem. In order to make the pupil tracking estimation more accurate, this work uses the YOLOv3-tiny [38] network-based deep-learning design to detect and track the position of the pupil. The proposed pupil detector is a well-trained deep-learning network model. Compared with the previous designs, the proposed method reduces the pupil tracking errors caused by light reflection and shadow interference in visible-light mode, and will also improve the accuracy of the calibration process of the entire eye tracking system. Therefore, the gaze tracking function of the wearable eye tracker will become powerful under visible-light conditions. The main contribution of this study is that the pupil location in nearfield visible-light eye images is detected precisely by the YOLOv3-tiny-based deep-learning technology. To our best knowledge from surveying related studies, the novelty of this work is to provide the leading reference design of deep-learning-based pupil tracking technology using the nearfield visible-light eye images for the application of wearable gaze tracking devices. Moreover, the other novelties and advantages of the proposed deep-learning-based pupil tracking technology are described as follows:  
1. By using a nearfield visible-light eye image dataset for training, the used deep-learning models achieved real-time and accurate detection of the pupil position at the visible-light mode. 2. The proposed design detects the position of the pupil's object at any eyeball movement condition, which is more effective than the traditional image processing methods without deep-learning technologies at the near-eye visible-light mode. 3. The proposed pupil tracking technology can overcome efficiently the light and shadow interferences at the near-eye visible-light mode, and the detection accuracy of a pupil's