  Gaze estimation has grown rapidly in accuracy in recent years. However, these
models often fail to take advantage of different computer vision (CV)
algorithms and techniques (such as small ResNet and Inception networks and
ensemble models) that have been shown to improve results for other CV problems.
Additionally, most current gaze estimation models require the use of either
both eyes or an entire face, whereas real-world data may not always have both
eyes in high resolution. Thus, we propose a gaze estimation model that
implements the ResNet and Inception model architectures and makes predictions
using only one eye image. Furthermore, we propose an ensemble calibration
network that uses the predictions from several individual architectures for
subject-specific predictions. With the use of lightweight architectures, we
achieve high performance on the GazeCapture dataset with very low model
parameter counts. When using two eyes as input, we achieve a prediction error
of 1.591 cm on the test set without calibration and 1.439 cm with an ensemble
calibration model. With just one eye as input, we still achieve an average
prediction error of 2.312 cm on the test set without calibration and 1.951 cm
with an ensemble calibration model. We also notice significantly lower errors
on the right eye images in the test set, which could be important in the design
of future gaze estimation-based tools.
