RavenGaze: A Dataset for Gaze Estimation Leveraging Psychological Experiment Through Eye Tracker

One major challenge in appearance-based gaze estimation is the lack of high-quality labeled data. Establishing databases or datasets is a way to obtain accurate gaze data and test methods or tools. However, the methods of collecting data in existing databases are designed on artificial chasing target tasks or unintentional free-looking tasks, which are not natural and real eye interactions and cannot reflect the inner cognitive processes of humans. To fill this gap, we propose the first gaze estimation dataset collected from an actual psychological experiment by the eye tracker, called the RavenGaze dataset. We design an experiment employing Raven's Matrices as visual stimuli and collecting gaze data, facial videos as well as screen content videos simultaneously. Thirty-four participants were recruited. The results show that the existing algorithms perform well on our RavenGaze dataset in the 3D and 2D gaze estimation task, and demonstrate good generalization ability according to cross-dataset evaluation task. RavenGaze and the establishment of the benchmark lay the foundation for other researchers to do further in-depth research and test their methods or tools. Our dataset is available at https://intelligentinteractivelab.github.io/datasets/RavenGaze/index.html.