A single-camera gaze tracking system under natural light

Gaze tracking is a human-computer interaction technology, and it has been widely studied in the academic and industrial fields. However, constrained by the performance of the specific sensors and algorithms, it has not been popularized for everyone. This paper proposes a single-camera gaze tracking system under natural light to enable its versatility. The iris center and anchor point are the most crucial factors for the accuracy of the system. The accurate iris center is detected by the simple active contour snakuscule, which is initialized by the prior knowledge of eye anatomical dimensions. After that, a novel anchor point is computed by the stable facial landmarks. Next, second-order mapping functions use the eye vectors and the head pose to estimate the points of regard. Finally, the gaze errors are improved by implementing a weight coefficient on the points of regard of the left and right eyes. The feature position of the iris center achieves an accuracy of 98.87% on the GI4E database when the normalized error is lower than 0.05. The accuracy of the gaze tracking method is superior to the-state-of-the-art appearance-based and feature-based methods on the EYEDIAP database

Accurate pupil center detection in off-the-shelf eye tracking systems using convolutional neural networks

Remote eye tracking technology has suffered an increasing growth in recent years due to its applicability in many research areas. In this paper, a video-oculography method based on convolutional neural networks (CNNs) for pupil center detection over webcam images is proposed. As the first contribution of this work and in order to train the model, a pupil center manual labeling procedure of a facial landmark dataset has been performed. The model has been tested over both real and synthetic databases and outperforms state-of-the-art methods, achieving pupil center estimation errors below the size of a constricted pupil in more than 95% of the images, while reducing computing time by a 8 factor. Results show the importance of use high quality training data and well-known architectures to achieve an outstanding performance.This research was funded by Public University of Navarra (Pre-doctoral research grant) and by the Spanish Ministry of Science and Innovation under Contract 'Challenges of Eye Tracking Off-the-Shelf (ChETOS)' with reference: PID2020-118014RB-I0