GEDDnet: A Network for Gaze Estimation with Dilation and Decomposition

Appearance-based gaze estimation from RGB images provides relatively unconstrained gaze tracking from commonly available hardware. The accuracy of subject-independent models is limited partly by small intra-subject and large inter-subject variations in appearance, and partly by a latent subject-dependent bias. To improve estimation accuracy, we propose to use dilated-convolutions in a deep convolutional neural network to capture subtle changes in the eye images, and a novel gaze decomposition method that decomposes the gaze angle into the sum of a subject-independent gaze estimate from the image and a subject-dependent bias. To further reduce estimation error, we propose a calibration method that estimates the bias from a few images taken as the subject gazes at only a few or even just a single gaze target. This significantly redues calibration time and complexity. Experiments on four datasets, including a new dataset we collected containing large variations in head pose and face location, indicate that even without calibration the estimator already outperforms state-of-the-art methods by more than 6.3%. The proposed calibration method is robust to the location of calibration target and reduces estimation error significantly (up to 35.6%), achieving state-of-the-art performance with much less calibration data than required by previously proposed methods.