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The present thesis deals with the numerical modeling of facial tissue biomechanics for the prediction of the patient's postoperative appearance in cranio-, dento-maxillofacial surgery planning. Typically, craniofacial surgery interventions consist in the rearrangement of facial bones, which induces the deformation of surrounding soft tissue. The postoperative patient's appearance has to be predicted on the basis of 3D geometrical models derived from tomographic data and the prescribed displacements of relocated bone structures. The linear elastic approach known from the previous works and widely used in soft tissue modeling is generally limited by the assumption of small deformations and implies a substantial error by ad hoc calculations of large deformations. The investigations carried out in this work lead to the development of a more accurate and flexible non-linear elastic approach. Different tissue types are modeled as a homogeneous, isotropic, quasi-incompressible St. Venant-Kirchhoff material characterized by two elastic constants, the Young modulus describing the material stiffness and the Poisson ratio describing the material compressibility. The numerical solution of the associated discrete boundary value problem on tetrahedral grids is obtained via the finite element method (FEM). Since the non-linear elastic FEM is in general very expensive, an adaptive numerical scheme for the efficient and robust computation of both small and large deformations with user-defined precision is developed. The modelling approach has been validated by the direct comparison between the simulation outcome and the patient's postoperative facial outline in a number of clinical studies. In addition to the static soft tissue prediction, we present a new approach for the estimation of individual facial emotion expressions. We assume natural physiological mechanism of facial expressions that is the impact of contracting muscles on remaining facial tissue. Using this technique, single muscle actions as well as complex individual facial emotion expressions can be estimated.