Estimating the head pose of a person is a crucial problem that has a large amount of applications such as aiding in gaze estimation, modeling attention, fitting 3D models to video and performing face alignment.
|Trend||Dataset||Best Method||Paper title||Paper||Code||Compare|
With an ever-increasing number of mobile devices competing for our attention, quantifying when, how often, or for how long users visually attend to their devices has emerged as a core challenge in mobile human-computer interaction.
Specifically, the rectangular coordinates of only four non-coplanar feature points from a predefined 3D facial model as well as the corresponding ones automatically/ manually extracted from a 2D face image are first normalized to exclude the effect of external factors (i. e., scale factor and translation parameters).
Head poses are a key component of human bodily communication and thus a decisive element of human-computer interaction.
We address a problem of estimating pose of a person's head from its RGB image.
Traditional approaches to head pose estimation heavily relies on the accuracy of facial landmarks, and solve the correspondence problem between 2D facial landmarks and a mean 3D head model (ad-hoc fitting procedures), which seriously limited their performance, especially when the visibility of face is not in good condition.
In this paper, we address the problem of how to robustly train a ConvNet for regression, or deep robust regression.
Head pose estimation and tracking is useful in variety of medical applications.
In this paper we show the importance of the head pose estimation in the task of trajectory forecasting.