  In this paper, a method to mimic a human face and eyes is proposed which can
be regarded as a combination of computer vision techniques and neural network
concepts. From a mechanical standpoint, a 3-DOF spherical parallel robot is
used which imitates the human head movement. In what concerns eye movement, a
2-DOF mechanism is attached to the end-effector of the 3-DOF spherical parallel
mechanism. In order to have robust and reliable results for the imitation,
meaningful information should be extracted from the face mesh for obtaining the
pose of a face, i.e., the roll, yaw, and pitch angles. To this end, two methods
are proposed where each of them has its own pros and cons. The first method
consists in resorting to the so-called Mediapipe library which is a machine
learning solution for high-fidelity body pose tracking, introduced by Google.
As the second method, a model is trained by a linear regression model for a
gathered dataset of face pictures in different poses. In addition, a 3-DOF
Agile Eye parallel robot is utilized to show the ability of this robot to be
used as a system which is similar to a human head for performing a 3-DOF
rotational motion pattern. Furthermore, a 3D printed face and a 2-DOF eye
mechanism are fabricated to display the whole system more stylish way.
Experimental tests, which are done based on a ROS platform, demonstrate the
effectiveness of the proposed methods for tracking the human head and eye
movement.
