Face Reconstruction from Skull Shapes and Physical Attributes

Paysan, Pascal; Lüthi, Marcel; Albrecht, Thomas; Lerch, Anita; Amberg, Brian; Santini, Francesco; Vetter, Thomas

doi:10.1007/978-3-642-03798-6_24

Cited by 43 publications

(35 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An alternative to CT scanners is the use of MRI scanners [20,25], which are considered not to be harmful. Again dense soft-tissue depth measurements can be acquired, including differentiation between different types of soft-tissue (muscle, fat).…”

Section: Craniofacial Informationmentioning

confidence: 99%

See 1 more Smart Citation

Computerized craniofacial reconstruction: Conceptual framework and review

Claes

Vandermeulen

Greef

et al. 2010

Forensic Science International

123

View full text Add to dashboard Cite

Section: Craniofacial Informationmentioning

confidence: 99%

“…A linear statistical interpolation of attribute differences is for example used in [18,19] in a postreconstruction mode and in Refs. [20,21] in a pre-reconstruction mode. A linear geometrical interpolation between two reconstructions with different skull properties is used to generate intermediate reconstruction results in Ref.…”

Section: Craniofacial Templatementioning

confidence: 99%

Computerized craniofacial reconstruction: Conceptual framework and review

Claes

Vandermeulen

Greef

et al. 2010

Forensic Science International

123

View full text Add to dashboard Cite

“…Other instances however might be amenable to joint modelling. Paysan et al [66] made an attempt to learn the relationship between skull-shapes and face-shapes. They use three sets of training data.…”

Section: Joint Instance Modellingmentioning

confidence: 99%

Inverse Rendering of Faces with a 3D Morphable Model

Aldrian

Smith

2013

IEEE Trans. Pattern Anal. Mach. Intell.

179

160

View full text Add to dashboard Cite

In this thesis, we present a complete framework to inverse render faces with a 3D Morphable Model. By decomposing the image formation process into a geometric and photometric part, we are able to state the problem as a multilinear system which can be solved accurately and efficiently. As we treat each contribution as independent, the objective function is convex in the parameters and a globally optimal solution can be found. We start by recovering 3D shape using a novel algorithm which incorporates generalisation errors of the model obtained from empirical measurements. The algorithm is extended so it can efficiently deal with mixture distributions. We then describe three methods to recover facial texture, and for the second and third, diffuse lighting, specular reflectance and camera properties from a single image. These methods make increasingly weak assumptions and can all be solved in a linear fashion. We further modify our framework so it accounts for global illumination effects. This is achieved by incorporating statistical models for ambient occlusion and bent normals into the image formation model. We show that solving for ambient occlusion and bent normal parameters as part of the fitting process improves the accuracy of the estimated texture map and illumination environment. We present results on challenging data, rendered under complex natural illumination with both specular reflectance and occlusion of the illumination environment. We evaluate our findings on publicly available datasets, where we are able to obtain state-ofthe-art results. Finally, we present a practical method to synthesise a larger population from a small training-set and show how the new instances can be used to build a flexible PCA model.

show abstract

“…The reconstruction result is generally determined by the experience of practitioners. To reduce reconstruction time and eliminate subjective biases, different computeraid craniofacial reconstruction methods have been proposed [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. The state-of-the-art of the computer-aid craniofacial reconstruction have comprehensively been reviewed in the surveys [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…So many deformation methods are proposed to model the non-rigid shape deformation of http://asp.eurasipjournals.com/content/2012/1/217 skull and face, such as radial basis functions (RBF) [22,23], or more exactly, a thin plate spline (TPS)-based deformation [12,24,25] for its smoothness. Instead of using fixed template, the recently proposed statistical craniofacial reconstruction methods [12][13][14][15][16][17] construct a type of deformable model from a set of 3D heads by the principle component analysis (PCA) technique. The statistic deformable model can be regarded as a dynamic template for the given skull.…”

Section: Introductionmentioning

confidence: 99%

Craniofacial reconstruction based on a hierarchical dense deformable model

Duan

Zhou

et al. 2012

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Craniofacial reconstruction from skull has deeply been investigated by computer scientists in the past two decades because it is important for identification. The dominant methods construct facial surface from the soft tissue thickness measured at a set of skull landmarks. The quantity and position of the landmarks are very vital for craniofacial reconstruction, but there is no standard. In addition, it is difficult to accurately locate the landmarks on dense mesh without manual assistance. In this article, we propose an automatic craniofacial reconstruction method based on a hierarchical dense deformable model. To construct the model, we collect more than 100 head samples by computerized tomography scanner. The samples are represented as dense triangle mesh to model face and skull shape. As the deformable model demands all samples in uniform form, a non-rigid registration algorithm is presented to align the samples in point-to-point correspondence. Based on the aligned samples, a global deformable model is constructed, and three local models are constructed from the segmented patches of the eye, nose, and mouth. For a given skull, the global and local deformable models are matched with it, and the reconstructed facial surface is obtained by fusing the global and local reconstruction results. To validate our method, a face deformable model is constructed and the reconstruction results are evaluated in its coefficient domain. The experimental results indicate that the proposed method has good performance for craniofacial reconstruction.

show abstract

Face Reconstruction from Skull Shapes and Physical Attributes

Cited by 43 publications

References 10 publications

Computerized craniofacial reconstruction: Conceptual framework and review

Computerized craniofacial reconstruction: Conceptual framework and review

Inverse Rendering of Faces with a 3D Morphable Model

Craniofacial reconstruction based on a hierarchical dense deformable model

Contact Info

Product

Resources

About