Gaze Estimation is a task to predict where a person is looking at given the person’s full face. The task contains two directions: 3-D gaze vector and 2-D gaze position estimation. 3-D gaze vector estimation is to predict the gaze vector, which is usually used in the automotive safety. 2-D gaze position estimation is to predict the horizontal and vertical coordinates on a 2-D screen, which allows utilizing gaze point to control a cursor for human-machine interaction.
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One challenging task, gaze estimation in the wild, concerns data collected in unconstrained environments with varying camera-person distances, like the Gaze360 dataset.
Human-Computer Interaction(HCI) is a field that studies interactions between human users and computer systems.
To this end, we designed a data-collection protocol and evaluation scheme geared towards providing a faithful portrayal of the real-world usage of Pupil Invisible glasses.
In our experiments, results showed a decrease of the average angular error by 2. 38% when compared to state-of-the-art methods on the MPIIFaceGaze data set, and a second-place on the EyeDiap data set.
We show that our dataset can significantly improve the robustness of gaze estimation methods across different head poses and gaze angles.
In this paper, we evaluate a synthetic framework to be used in the field of gaze estimation employing deep learning techniques.
Moving beyond the dataset, we propose a novel deep model for joint gaze estimation and action recognition in FPV.
The magnitude of contribution from temporal gaze trace is yet unclear for higher resolution/frame rate imaging systems, in which more detailed information about an eye is captured.
We present the second edition of OpenEDS dataset, OpenEDS2020, a novel dataset of eye-image sequences captured at a frame rate of 100 Hz under controlled illumination, using a virtual-reality head-mounted display mounted with two synchronized eye-facing cameras.
In the 34 developed and 156 developing countries, there are about 132 million disabled people who need a wheelchair constituting 1. 86% of the world population.