Experimental Study on the Imitation of the Human Head-and-Eye Pose Using the 3-DOF Agile Eye Parallel Robot with ROS and Mediapipe Framework

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.

Experimental Study on the Imitation of the Human Neck-and-Eye Pose Using the 3-DOF Agile Eye Parallel Robot Based on a Deep Neural Network Approach

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 face 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 neck 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. Experiments on this platform demonstrate the effectiveness of the proposed methods for tracking the human neck and eye movement.