Real-time Appearance-based Gaze Estimation via Web-Camera

In this work, we aim at realizing an eye tracker functionality on an off-the-shelf low cost web-camera. In particular, we address the problem of appearance gaze estimation employing deep learning methods for solving the regression task of predicting the point of gaze on a display. This solution is validated using a commercial infrared eye tracker: we achieve 73.843 Root Mean Square Error (RMSE) in pixels between the predictions. The proposed solution outperforms a relevant web-cam solution in terms of pixel-normalized RMSE 0.022 against 0.116 within the carried out comparative study, respectively.