1 research outputs found
Single Image based Head Pose Estimation with Spherical Parameterization and 3D Morphing
Head pose estimation plays a vital role in various applications, e.g.,
driverassistance systems, human-computer interaction, virtual reality
technology, and so on. We propose a novel geometry based algorithm for
accurately estimating the head pose from a single 2D face image at a very low
computational cost. 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). Then, the four normalized 3D feature points are
represented in spherical coordinates with reference to the uniquely determined
sphere by themselves. Due to the spherical parameterization, the coordinates of
feature points can then be morphed along all the three directions in the
rectangular coordinates effectively. Finally, the rotation matrix indicating
the head pose is obtained by minimizing the Euclidean distance between the
normalized 2D feature points and the 2D re-projections of morphed 3D feature
points. Comprehensive experimental results over two popular databases, i.e.,
Pointing'04 and Biwi Kinect, demonstrate that the proposed algorithm can
estimate head poses with higher accuracy and lower run time than
state-of-the-art geometry based methods. Even compared with start-of-the-art
learning based methods or geometry based methods with additional depth
information, our algorithm still produces comparable performance.Comment: 34pages, 5figures, Journa