Abstract: Achieving realistic facial expressions is a central goal in humanoid robot head research. Current methods for determining facial drive points, often based on FACS and design experience, are subjective and prone to bias. This study proposes a quantitative method for selecting facial drive point positions based on optical motion capture and interpolation reconstruction, providing a set of 3D coordinates. On this basis, we further propose a rapid positioning method for robotic drive points applicable to diverse human bionic subjects. Motion data of 238 reflective markers were captured from volunteers performing six basic and one arbitrary facial expression to represent facial movement characteristics. Drive areas were defined based on facial muscle distribution and movement patterns, and the maximum displacement point in each area was calculated using TPS interpolation and displacement field reconstruction to obtain full-face drive point coordinates. For diverse human bionic subjects, proportional correspondences between their inherent facial features and the drive point parameters established in this study were calculated, and new facial drive points were positioned through scaling. To validate the approach, based on the results, a 19-degree-of-freedom humanoid head prototype was built, realizing six basic expressions with recognition rates exceeding 90%, thereby validating the feasibility of the proposed drive point determination method for practical applications. In summary, the proposed methods provide a quantitative reference for subsequent research, enhance design efficiency, and provides a theoretical foundation for achieving more natural and realistic robotic facial expressions.

Key words: Facial expression capture')">Facial expression capture, Interpolation reconstruction, Drive point positioning, Facial drive points, Humanoid robot head