Facial Expression Analysis, Drive Control Design, and Imitation Reproduction for Humanoid Robots

Humanoid robots with realistic facial expression capabilities hold significant potential in medical therapy, education, and the arts by replicating human emotions, thereby enhancing adaptive Human–Robot Interaction (HRI). This work presents a systematic framework for developing a facial system for humanoid robots. Our contributions are fourfold: First, a data-driven method for facial skin drive point selection using the Cohn-Kanade dataset, analyzing landmark displacement patterns to differentiate static and dynamic facial regions. Second, a modular biomimetic facial system integrating six region-optimized actuation units (eyebrows, jaw, eyelids, nasal alar, eyes, mouth corners) with a total of 14 servo motors, a 3D-printed head-shell with static-dynamic partitioning, and anthropomorphic silicone skin fabricated using custom molds. Third, an expression imitation algorithm using 11 morphological geometric features and a neural inverse model (Multilayer Neural Network, MNN) trained on 3000 automatically collected servo-image pairs. Fourth, experimental validation demonstrating accurate expression replication through custom metrics (Mean Error Percentage of Command, MEPC; Mean Error Percentage of Discretization Command, MEPDC) and expression similarity quantification. Our system achieves MEPC of 0.107 and MEPDC of 0.114, outperforming Linear regression, Random Forest, and Support Vector Regression (SVR) models. This work establishes a foundational approach for developing expressive humanoid robot facial systems, enabling emotionally intelligent HRI.