Estimating muscle activation patterns using a surrogate model of facial biomechanics

Abstract

Analyzing the muscle activities that drive the expressive facial gestures can be a useful tool in assessing one's emotional state of mind. Since the skin motion is much easier to measure in comparison to the actual electrical excitation signal of facial muscles, a biomechanical model of the human face driven by these muscles can be a useful tool in relating the geometric information to the muscle activity. However, long computational time often hinders its practicality. The objective of this study was to replace the precise but computationally demanding biomechanical model by a much faster multivariate meta-model (surrogate model), such that a significant speedup (real-time interactive speed) can be achieved and data from the biomechanical model can be practically exploited. Using the proposed surrogate, muscle activation patterns of six key facial expressions were estimated in the iterative fit from the structured-light scanned geometric information.