Emulating facial biomechanics using multivariate partial least squares surrogate models
Journal article, Peer reviewed
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Original versionInternational Journal for Numerical Methods in Biomedical Engineering 2014, 30(11):1103-1120 10.1002/cnm.2646
A detailed biomechanical model of the human face driven by a network of muscles is a useful tool in relating the muscle activities to facial deformations. However, lengthy computational times often hinder its applications in practical settings. The objective of this study is to replace precise but computationally demanding biomechanical model by a much faster multivariate meta-model (surrogate model), such that a significant speedup (to real-time interactive speed) can be achieved. Using a multilevel fractional factorial design, the parameter space of the biomechanical system was probed from a set of sample points chosen to satisfy maximal rank optimality and volume filling. The input–output relationship at these sampled points was then statistically emulated using linear and nonlinear, cross-validated, partial least squares regression models. It was demonstrated that these surrogate models can mimic facial biomechanics efficiently and reliably in real-time.
This is the author’s final, accepted and refereed manuscript to the article. Locked until 2015-05-06