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

One Eye is All You Need: Lightweight Ensembles for Gaze Estimation with Single Encoders