A Differential Approach for Gaze Estimation

Non-invasive gaze estimation methods usually regress gaze directions directly from a single face or eye image. However, due to important variabilities in eye shapes and inner eye structures amongst individuals, universal models obtain limited accuracies and their output usually exhibit high variance as well as biases which are subject dependent. Therefore, increasing accuracy is usually done through calibration, allowing gaze predictions for a subject to be mapped to his/her actual gaze. In this paper, we introduce a novel image differential method for gaze estimation. We propose to directly train a differential convolutional neural network to predict the gaze differences between two eye input images of the same subject. Then, given a set of subject specific calibration images, we can use the inferred differences to predict the gaze direction of a novel eye sample. The assumption is that by allowing the comparison between two eye images, annoyance factors (alignment, eyelid closing, illumination perturbations) which usually plague single image prediction methods can be much reduced, allowing better prediction altogether. Experiments on 3 public datasets validate our approach which constantly outperforms state-of-the-art methods even when using only one calibration sample or when the latter methods are followed by subject specific gaze adaptation.Comment: Extension to our paper A differential approach for gaze estimation with calibration (BMVC 2018) Submitted to PAMI on Aug. 7th, 2018 Accepted by PAMI short on Dec. 2019, in IEEE Transactions on Pattern Analysis and Machine Intelligenc

Gaze Estimation in the 3D Space Using RGB-D Sensors

We address the problem of 3D gaze estimation within a 3D environment from remote sensors, which is highly valuable for applications in human-human and human-robot interactions. To the contrary of most previous works, which are limited to screen gazing applications, we propose to leverage the depth data of RGB-D cameras to perform an accurate head pose tracking, acquire head pose invariance through a 3D rectification process that renders head pose dependent eye images into a canonical viewpoint, and computes the line-of-sight in the 3D space. To address the low resolution issue of the eye image resulting from the use of remote sensors, we rely on the appearance based gaze estimation paradigm, which has demonstrated robustness against this factor. In this context, we do a comparative study of recent appearance based strategies within our framework, study the generalization of these methods to unseen individual, and propose a cross-user eye image alignment technique relying on the direct registration of gaze-synchronized eye images. We demonstrate the validity of our approach through extensive gaze estimation experiments on a public dataset as well as a gaze coding task applied to natural job interviews