Development and validation of a three-dimensional finite element model of the face

Finite element analysis of biomechanical interactions of a subcutaneous suspension suture and human face soft-tissue: a cadaver study

In order to study the local interactions between facial soft-tissues and a Silhouette Soft® suspension suture, a CE marked medical device designed for the repositioning of soft tissues in the face and the neck, Finite element simulations were run, in which a model of the suture was embedded in a three-layer Finite Element structure that accounts for the local mechanical organization of human facial soft tissues. A 2D axisymmetric model of the local interactions was designed in ANSYS, in which the geometry of the tissue, the boundary conditions and the applied loadings were considered to locally mimic those of human face soft tissue constrained by the suture in facial tissue repositioning. The Silhouette Soft suture is composed of a knotted thread and sliding cones that are anchored in the tissue. Hence, simulating these interactions requires special attention for an accurate modelling of contact mechanics. As tissue is modelled as a hyper-elastic material, the displacement of the facial soft tissue changes in a nonlinear way with the intensity of stress induced by the suture and the number of the cones. Our simulations show that for a 4-cone suture a displacement of 4.35 mm for a 2.0 N external loading and of 7.6 mm for 4.0 N. Increasing the number of cones led to the decrease in the equivalent local strain (around 20%) and stress (around 60%) applied to the tissue. The simulated displacements are in general agreement with experimental observations.

Effects of miniplate anchored Herbst appliance on skeletal, dental and masticatory structures of the craniomandibular apparatus: A finite element study

OBJECTIVE

To analyze the stress distribution in the hard and soft tissue structures of craniomandibular complex during mandibular advancement with miniplate anchored rigid fixed functional appliance (FFA) using Finite Element Analysis (FEA).

MATERIAL AND METHODS

The virtual model consisting of all the maxillofacial bones (up to calvaria), the mandible and temporomandibular joint (TMJ) was generated using the volumetric data from pre-treatment CBCT-scan of a growing patient. The masticatory muscles, other soft tissues, Herbst appliance and plate geometry were modelled mathematically. Force vectors simulating muscle contraction at rest and advanced mandibular positions, with protraction force of 8N were applied. The final model was imported into ANSYS for analysis after assigning material properties.

RESULTS

The maximum von Mises stress of 11.69MPa and 11.96MPa magnitude was observed in the region of pterygoid plates and at the bone-miniplate interface respectively, with the mandibular advancement of 7mm. Stress patterns were also noted at the condylar neck. The stress values observed in the medial and lateral pterygoid muscles were of 10.42MPa and 4.16MPa magnitude, respectively. Stress was noted in the bucco-cervical region of the upper posterior teeth, but negligible change was seen on the lower anterior teeth and periodontal ligament.

CONCLUSION

Miniplate Anchored Herbst Appliance brought about Class II skeletal correction in growing children as it was accompanied by minimal changes in the inclination of the lower incisors. Soft tissue structures like pterygoid muscles and discal ligaments exhibited increased stress whereas masseter muscle displayed reduction in stresses.

Implications of Applying New Technology in Cosmetic and Reconstructive Facial Plastic Surgery

The field of facial plastic and reconstructive surgery is privy to a myriad of technological advancements. As innovation in areas such as imaging, computer applications, and biomaterials progresses at breakneck speed, the potential for clinical application is endless. This review of recent progress in the implementation of new technologies in facial plastic surgery highlights some of the most innovative and impactful developments in the past few years of literature. Patient-specific surgical modeling has become the gold standard for oncologic and posttraumatic reconstructive surgery, with demonstrated improvements in operative times, restoration of anatomical structure, and patient satisfaction. Similarly, reductions in revision rates with improvements in learner technical proficiency have been noted with the use of patient-specific models in free flap reconstruction. In the cosmetic realm, simulation-based rhinoplasty implants have drastically reduced operative times while concurrently raising patient postoperative ratings of cosmetic appearance. Intraoperative imaging has also seen recent expansion in its adoption driven largely by reports of eradication of postoperative imaging and secondary-often complicated-revision reconstructions. A burgeoning area likely to deliver many advances in years to come is the integration of bioprinting into reconstructive surgery. Although yet to clearly make the translational leap, the implications of easily generatable induced pluripotent stem cells in replacing autologous, cadaveric, or synthetic tissues in surgical reconstruction are remarkable.

A Systematic Review of Continuum Modeling of Skeletal Muscles: Current Trends, Limitations, and Recommendations

Finite elasticity theory has been commonly used to model skeletal muscle. A very large range of heterogeneous constitutive laws has been proposed. In this review, the most widely used continuum models of skeletal muscles were synthetized and discussed. Trends and limitations of these laws were highlighted to propose new recommendations for future researches. A systematic review process was performed using two reliable search engines as PubMed and ScienceDirect. 40 representative studies (13 passive muscle materials and 27 active muscle materials) were included into this review. Note that exclusion criteria include tendon models, analytical models, 1D geometrical models, supplement papers, and indexed conference papers. Trends of current skeletal muscle modeling relate to 3D accurate muscle representation, parameter identification in passive muscle modeling, and the integration of coupled biophysical phenomena. Parameter identification for active materials, assumed fiber distribution, data assumption, and model validation are current drawbacks. New recommendations deal with the incorporation of multimodal data derived from medical imaging, the integration of more biophysical phenomena, and model reproducibility. Accounting for data uncertainty in skeletal muscle modeling will be also a challenging issue. This review provides, for the first time, a holistic view of current continuum models of skeletal muscles to identify potential gaps of current models according to the physiology of skeletal muscle. This opens new avenues for improving skeletal muscle modeling in the framework of in silico medicine.

Experimental and Numerical Characterization of the Mechanical Masseter Muscle Response During Biting

Predictive simulations of the mastication system would significantly improve our understanding of temporomandibular joint (TMJ) disorders and the planning of cranio-maxillofacial surgery procedures. Respective computational models must be validated by experimental data from in vivo characterization of the mastication system's mechanical response. The present pilot-study demonstrates the feasibility of a combined experimental and numerical procedure to validate a computer model of the masseter muscle. An experimental setup is proposed that provides a simultaneous bite force measurement and ultrasound-based visualization of muscle deformation. The direct comparison of the experimentally observed and numerically predicted muscle response demonstrates the predictive capabilities of such anatomically accurate biting models. Differences between molar and incisor biting are investigated; muscle deformation is recorded for three different bite forces in order to capture the effect of increasing muscle fiber recruitment. The three-dimensional (3D) muscle deformation at each bite position and force-level is approximatively reconstructed from ultrasound measurements in five distinct cross-sectional areas (four horizontal and one vertical cross section). The experimental work is accompanied by numerical simulations to validate the predictive capabilities of a constitutive muscle model previously formulated. An anatomy-based, fully 3D model of the masseter muscle is created from magnetic resonance images (MRI) of the same subject. The direct comparison of experimental and numerical results revealed good agreement for maximum bite forces and masseter deformations in both biting positions. The present work therefore presents a feasible in vivo measurement system to validate numerically predicted masseter muscle contractions during mastication.

IMAGE-BASED SKELETAL MUSCLE COORDINATION: CASE STUDY ON A SUBJECT SPECIFIC FACIAL MIMIC SIMULATION

Facial muscle coordination is a fundamental mechanism for facial mimics and expressions. The understanding of this complex mechanism leads to better diagnosis and treatment of facial disorders like facial palsy or disfigurement. The objective of this work was to use magnetic resonance imaging (MRI) technique to characterize the activation behavior of facial muscles and then simulate their coordination mechanism using a subject specific finite element model. MRI data of lower head of a healthy subject were acquired in neutral and in the pronunciation of the sound [o] positions. Then, a finite element model was derived directly from acquired MRI images in neutral position. Transversely-isotropic, hyperelastic, quasi-incompressible behavior law was implemented for modeling facial muscles. The simulation to produce the pronunciation of the sound [o] was performed by the cumulative coordination between three pairs of facial mimic muscles (Zygomaticus Major (ZM), Levator Labii Superioris (LLS), Levator Anguli Oris (LAO)). Mean displacement amplitude showed a good agreement with a relative deviation of 15% between numerical outcome and MRI-based measurement when all three muscles are involved. This study elucidates, for the first time, the facial muscle coordination using in vivo data leading to improve the model understanding and simulation outcomes.

sEMG-assisted inverse modelling of 3D lip movement: a feasibility study towards person-specific modelling

We propose a surface-electromyographic (sEMG) assisted inverse-modelling (IM) approach for a biomechanical model of the face to obtain realistic person-specific muscle activations (MA) by tracking movements as well as innervation trajectories. We obtained sEMG data of facial muscles and 3D positions of lip markers in six volunteers and, using a generic finite element (FE) face model in ArtiSynth, performed inverse static optimisation with and without sEMG tracking on both simulation data and experimental data. IM with simulated data and experimental data without sEMG data showed good correlations of tracked positions (0.93 and 0.67) and poor correlations of MA (0.27 and 0.20). When utilising the sEMG-assisted IM approach, MA correlations increased drastically (0.83 and 0.59) without sacrificing performance in position correlations (0.92 and 0.70). RMS errors show similar trends with an error of 0.15 in MA and of 1.10 mm in position. Therefore, we conclude that we were able to demonstrate the feasibility of an sEMG-assisted inverse modelling algorithm for the perioral region. This approach may help to solve the ambiguity problem in inverse modelling and may be useful, for instance, in future applications for preoperatively predicting treatment-related function loss.

Location-specific mechanical response and morphology of facial soft tissues

The facial tissue of 9 healthy volunteers (m/f; age: 23-60y) is characterized at three different locations using a procedure combining suction measurements and 18MHz ultrasound imaging. The time-dependent and multilayered nature of skin is accounted for by adopting multiple loading protocols which differ with respect to suction probe opening size and rate of tissue deformation. Over 700 suction measurements were conducted and analyzed according to location-specific mechanical and morphological characteristics. All corresponding data are reported and made available for facial tissue analysis and biomechanical modeling. Higher skin stiffness is measured at the forehead in comparison to jaw and parotid; these two regions are further characterized by lower creep deformation. Thicker tissue regions display a tendency towards a more compliant and less dissipative response. Comparison of superficial layer thickness and corresponding mechanical measurements suggests that connective tissue density determines the resistance to deformation in suction experiments.

An eFTD-VP framework for efficiently generating patient-specific anatomically detailed facial soft tissue FE mesh for craniomaxillofacial surgery simulation

Accurate surgical planning and prediction of craniomaxillofacial surgery outcome requires simulation of soft tissue changes following osteotomy. This can only be achieved by using an anatomically detailed facial soft tissue model. The current state-of-the-art of model generation is not appropriate to clinical applications due to the time-intensive nature of manual segmentation and volumetric mesh generation. The conventional patient-specific finite element (FE) mesh generation methods are to deform a template FE mesh to match the shape of a patient based on registration. However, these methods commonly produce element distortion. Additionally, the mesh density for patients depends on that of the template model. It could not be adjusted to conduct mesh density sensitivity analysis. In this study, we propose a new framework of patient-specific facial soft tissue FE mesh generation. The goal of the developed method is to efficiently generate a high-quality patient-specific hexahedral FE mesh with adjustable mesh density while preserving the accuracy in anatomical structure correspondence. Our FE mesh is generated by eFace template deformation followed by volumetric parametrization. First, the patient-specific anatomically detailed facial soft tissue model (including skin, mucosa, and muscles) is generated by deforming an eFace template model. The adaptation of the eFace template model is achieved by using a hybrid landmark-based morphing and dense surface fitting approach followed by a thin-plate spline interpolation. Then, high-quality hexahedral mesh is constructed by using volumetric parameterization. The user can control the resolution of hexahedron mesh to best reflect clinicians' need. Our approach was validated using 30 patient models and 4 visible human datasets. The generated patient-specific FE mesh showed high surface matching accuracy, element quality, and internal structure matching accuracy. They can be directly and effectively used for clinical simulation of facial soft tissue change.

Improved Rubin-Bodner model for the prediction of soft tissue deformations

In craniomaxillofacial (CMF) surgery, a reliable way of simulating the soft tissue deformation resulted from skeletal reconstruction is vitally important for preventing the risks of facial distortion postoperatively. However, it is difficult to simulate the soft tissue behaviors affected by different types of CMF surgery. This study presents an integrated bio-mechanical and statistical learning model to improve accuracy and reliability of predictions on soft facial tissue behavior. The Rubin-Bodner (RB) model is initially used to describe the biomechanical behavior of the soft facial tissue. Subsequently, a finite element model (FEM) computers the stress of each node in soft facial tissue mesh data resulted from bone displacement. Next, the Generalized Regression Neural Network (GRNN) method is implemented to obtain the relationship between the facial soft tissue deformation and the stress distribution corresponding to different CMF surgical types and to improve evaluation of elastic parameters included in the RB model. Therefore, the soft facial tissue deformation can be predicted by biomechanical properties and statistical model. Leave-one-out cross-validation is used on eleven patients. As a result, the average prediction error of our model (0.7035mm) is lower than those resulting from other approaches. It also demonstrates that the more accurate bio-mechanical information the model has, the better prediction performance it could achieve.

Prediction of soft tissue deformations after CMF surgery with incremental kernel ridge regression

Facial soft tissue deformation following osteotomy is associated with the corresponding biomechanical characteristics of bone and soft tissues. However, none of the methods devised to predict soft tissue deformation after osteotomy incorporates population-based statistical data. The aim of this study is to establish a statistical model to describe the relationship between biomechanical characteristics and soft tissue deformation after osteotomy. We proposed an incremental kernel ridge regression (IKRR) model to accomplish this goal. The input of the model is the biomechanical information computed by the Finite Element Method (FEM). The output is the soft tissue deformation generated from the paired pre-operative and post-operative 3D images. The model is adjusted incrementally with each new patient's biomechanical information. Therefore, the IKRR model enables us to predict potential soft tissue deformations for new patient by using both biomechanical and statistical information. The integration of these two types of data is critically important for accurate simulations of soft-tissue changes after surgery. The proposed method was evaluated by leave-one-out cross-validation using data from 11 patients. The average prediction error of our model (0.9103mm) was lower than some state-of-the-art algorithms. This model is promising as a reliable way to prevent the risk of facial distortion after craniomaxillofacial surgery.

An eFace-Template Method for Efficiently Generating Patient-Specific Anatomically-Detailed Facial Soft Tissue FE Models for Craniomaxillofacial Surgery Simulation

Accurate surgical planning and prediction of craniomaxillofacial surgery outcome requires simulation of soft-tissue changes following osteotomy. This can only be accomplished on an anatomically-detailed facial soft tissue model. However, current anatomically-detailed facial soft tissue model generation is not appropriate for clinical applications due to the time intensive nature of manual segmentation and volumetric mesh generation. This paper presents a novel semi-automatic approach, named eFace-template method, for efficiently and accurately generating a patient-specific facial soft tissue model. Our novel approach is based on the volumetric deformation of an anatomically-detailed template to be fitted to the shape of each individual patient. The adaptation of the template is achieved by using a hybrid landmark-based morphing and dense surface fitting approach followed by a thin-plate spline interpolation. This methodology was validated using 4 visible human datasets (regarded as gold standards) and 30 patient models. The results indicated that our approach can accurately preserve the internal anatomical correspondence (i.e., muscles) for finite element modeling. Additionally, our hybrid approach was able to achieve an optimal balance among the patient shape fitting accuracy, anatomical correspondence and mesh quality. Furthermore, the statistical analysis showed that our hybrid approach was superior to two previously published methods: mesh-matching and landmark-based transformation. Ultimately, our eFace-template method can be directly and effectively used clinically to simulate the facial soft tissue changes in the clinical application.

Simulated effects of head movement on contact pressures between headforms and N95 filtering facepiece respirators-part 1: headform model and validation

In a respirator fit test, a subject is required to perform a series of exercises that include moving the head up and down and rotating the head left and right. These head movements could affect respirator sealing properties during the fit test and consequently affect fit factors. In a model-based system, it is desirable to have similar capability to predict newly designed respirators. In our previous work, finite element modeling (FEM)-based contact simulation between a headform and a filtering facepiece respirator was carried out. However, the headform was assumed to be static or fixed. This paper presents the first part of a series study on the effect of headform movement on contact pressures-a new headform with the capability to move down (flexion), up (extension), and rotate left and right-and validation. The newly developed headforms were validated for movement by comparing the simulated cervical vertebrae rotation angles with experimental results from the literature.

Elastic-viscoplastic modeling of soft biological tissues using a mixed finite element formulation based on the relative deformation gradient

The characteristic highly nonlinear, time-dependent, and often inelastic material response of soft biological tissues can be expressed in a set of elastic-viscoplastic constitutive equations. The specific elastic-viscoplastic model for soft tissues proposed by Rubin and Bodner (2002) is generalized with respect to the constitutive equations for the scalar quantity of the rate of inelasticity and the hardening parameter in order to represent a general framework for elastic-viscoplastic models. A strongly objective integration scheme and a new mixed finite element formulation were developed based on the introduction of the relative deformation gradient-the deformation mapping between the last converged and current configurations. The numerical implementation of both the generalized framework and the specific Rubin and Bodner model is presented. As an example of a challenging application of the new model equations, the mechanical response of facial skin tissue is characterized through an experimental campaign based on the suction method. The measurement data are used for the identification of a suitable set of model parameters that well represents the experimentally observed tissue behavior. Two different measurement protocols were defined to address specific tissue properties with respect to the instantaneous tissue response, inelasticity, and tissue recovery.

Generating Facial Expressions Using an Anatomically Accurate Biomechanical Model

This paper presents a computational framework for modelling the biomechanics of human facial expressions. A detailed high-order (Cubic-Hermite) finite element model of the human head was constructed using anatomical data segmented from magnetic resonance images. The model includes a superficial soft-tissue continuum consisting of skin, the subcutaneous layer and the superficial Musculo-Aponeurotic system. Embedded within this continuum mesh, are 20 pairs of facial muscles which drive facial expressions. These muscles were treated as transversely-isotropic and their anatomical geometries and fibre orientations were accurately depicted. In order to capture the relative composition of muscles and fat, material heterogeneity was also introduced into the model. Complex contact interactions between the lips, eyelids, and between superficial soft tissue continuum and deep rigid skeletal bones were also computed. In addition, this paper investigates the impact of incorporating material heterogeneity and contact interactions, which are often neglected in similar studies. Four facial expressions were simulated using the developed model and the results were compared with surface data obtained from a 3D structured-light scanner. Predicted expressions showed good agreement with the experimental data.

Linear elastic properties of the facial soft tissues using an aspiration device: towards patient specific characterization

Biomechanical modeling of the facial soft tissue behavior is needed in aesthetic or maxillo-facial surgeries where the simulation of the bone displacements cannot accurately predict the visible outcome on the patient's face. Because these tissues have different nature and elastic properties across the face, depending on their thickness, and their content in fat or muscle, individualizing their mechanical parameters could increase the simulation accuracy. Using a specifically designed aspiration device, the facial soft tissues deformation is measured at four different locations (cheek, cheekbone, forehead, and lower lip) on 16 young subjects. The stiffness is estimated from the deformations generated by a set of negative pressures using an inverse analysis based on a Neo Hookean model. The initial Young's modulus of the cheek, cheekbone, forehead, and lower lip are respectively estimated to be 31.0 kPa±4.6, 34.9 kPa±6.6, 17.3 kPa±4.1, and 33.7 kPa±7.3. Significant intra-subject differences in tissue stiffness are highlighted by these estimations. They also show important inter-subject variability for some locations even when mean stiffness values show no statistical difference. This study stresses the importance of using a measurement device capable of evaluating the patient specific tissue stiffness during an intervention.

Multiaxial mechanical properties and constitutive modeling of human adipose tissue: a basis for preoperative simulations in plastic and reconstructive surgery

A preoperative simulation of soft tissue deformations during plastic and reconstructive surgery is desirable to support the surgeon's planning and to improve surgical outcomes. The current development of constitutive adipose tissue models, for the implementation in multilayer computational frameworks for the simulation of human soft tissue deformations, has proved difficult because knowledge of the required mechanical parameters of fat tissue is limited. Therefore, for the first time, human abdominal adipose tissues were mechanically investigated by biaxial tensile and triaxial shear tests. The results of this study suggest that human abdominal adipose tissues under quasi-static and dynamic multiaxial loadings can be characterized as a nonlinear, anisotropic and viscoelastic soft biological material. The nonlinear and anisotropic features are consequences of the material's collagenous microstructure. The aligned collagenous septa observed in histological investigations causes the anisotropy of the tissue. A hyperelastic model used in this study was appropriate to represent the quasi-static multiaxial mechanical behavior of fat tissue. The constitutive parameters are intended to serve as a basis for soft tissue simulations using the finite element method, which is an apparent method for obtaining promising results in the field of plastic and reconstructive surgery.

A finite element model of the face including an orthotropic skin model under in vivo tension

Computer models of the human face have the potential to be used as powerful tools in surgery simulation and animation development applications. While existing models accurately represent various anatomical features of the face, the representation of the skin and soft tissues is very simplified. A computer model of the face is proposed in which the skin is represented by an orthotropic hyperelastic constitutive model. The in vivo tension inherent in skin is also represented in the model. The model was tested by simulating several facial expressions by activating appropriate orofacial and jaw muscles. Previous experiments calculated the change in orientation of the long axis of elliptical wounds on patients' faces for wide opening of the mouth and an open-mouth smile (both 30(o)). These results were compared with the average change of maximum principal stress direction in the skin calculated in the face model for wide opening of the mouth (18(o)) and an open-mouth smile (25(o)). The displacements of landmarks on the face for four facial expressions were compared with experimental measurements in the literature. The corner of the mouth in the model experienced the largest displacement for each facial expression (∼11-14 mm). The simulated landmark displacements were within a standard deviation of the measured displacements. Increasing the skin stiffness and skin tension generally resulted in a reduction in landmark displacements upon facial expression.

On modelling large deformations of heterogeneous biological tissues using a mixed finite element formulation

This study addresses the issue of modelling material heterogeneity of incompressible bodies. It is seen that when using a mixed (displacement-pressure) finite element formulation, the basis functions used for pressure field may not be able to capture the nonlinearity of material parameters, resulting in pseudo-residual stresses. This problem can be resolved by modifying the constitutive relation using Flory's decomposition of the deformation gradient. A two-parameter Mooney-Rivlin constitutive relation is used to demonstrate the methodology. It is shown that for incompressible materials, the modification does not alter the mechanical behaviour described by the original constitutive model. In fact, the modified constitutive equation shows a better predictability when compared against analytical solutions. Two strategies of describing the material variation (i.e. linear and step change) are explained, and their solutions are evaluated for an ideal two-material interfacing problem. When compared with the standard tied coupling approach, the step change method exhibited a much better agreement because of its ability to capture abrupt changes of the material properties. The modified equation in conjunction with integration point-based material heterogeneity is then used to simulate the deformations of heterogeneous biological structures to illustrate its applications.

Incremental kernel ridge regression for the prediction of soft tissue deformations

This paper proposes a nonlinear regression model to predict soft tissue deformation after maxillofacial surgery. The feature which served as input in the model is extracted with finite element model (FEM). The output in the model is the facial deformation calculated from the preoperative and postoperative 3D data. After finding the relevance between feature and facial deformation by using the regression model, we establish a general relationship which can be applied to all the patients. As a new patient comes, we predict his/her facial deformation by combining the general relationship and the new patient's biomechanical properties. Thus, our model is biomechanical relevant and statistical relevant. Validation on eleven patients demonstrates the effectiveness and efficiency of our method.

Experimental and numerical study on the mechanical behavior of the superficial layers of the face

BACKGROUND/PURPOSE

This paper reports a study on the quasi-static mechanical response of the superficial soft tissue of the face, in particular the skin and the superficial muscoloaponeurotic system (SMAS) plus the superficial fat. The mechanical characterization of soft tissues represents one of the main uncertainties of previously developed numerical models for face simulation.

METHODS

Two instruments based on the suction method were used for collecting experimental data: the Cutometer(®) (2 mm probe aperture diameter) and the Aspiration device (8 mm). Tests were performed in five different regions of the face (jaw, nasolabial, parotideomasseteric, zygomatic and forehead) on the same subject whose magnetic resonance imaging (MRI) scans were used to generate a full 3D finite element model of the face and for whom a series of experimental results for different loading cases are already available. The mechanical parameters of the tissue layers were determined through an inverse finite element analysis. Anatomical data (tissue layers' thickness) were determined through the analysis of a set of high-resolution MRI scans and ultrasound measurements performed in the regions tested.

RESULTS

The results of Cutometer(®) measurements show a relatively homogeneous mechanical response in different face regions, while the results of aspiration device measurements, which involve deeper tissues, show a larger variability. Mechanical model parameters of the skin and SMAS were determined for two constitutive model equations: a hyperelastic model based on the Rubin-Bodner formulation and a reduced polynomial model of second order.

CONCLUSION

The results reported in this work suggest that for simulations of the global behavior of facial soft tissue, such as craniofacial and maxillofacial surgery planning, the skin could be considered as a layer of uniform thickness and of uniform mechanical response through the different regions. Additionally, mechanical models were determined for skin and SMAS that could be used for simulations of surgical procedures requiring a distinction between these tissue layers.