  Expression cloning plays an important role in facial expression synthesis. In
this paper, a novel algorithm is proposed for facial expression cloning. The
proposed algorithm first introduces a new elastic model to balance the global
and local warping effects, such that the impacts from facial feature diversity
among people can be minimized, and thus more effective geometric warping
results can be achieved. Furthermore, a muscle-distribution-based (MD) model is
proposed, which utilizes the muscle distribution of the human face and results
in more accurate facial illumination details. In addition, we also propose a
new distance-based metric to automatically select the optimal parameters such
that the global and local warping effects in the elastic model can be suitably
balanced. Experimental results show that our proposed algorithm outperforms the
existing methods.
