Craniofacial reconstruction as a prediction problem using a Latent Root Regression model

Abstract

Abstract. In this paper, we present a computer-assisted method for facial reconstruction. This method provides an estimation of the facial shape associated with unidentified skeletal remains. Current computer-assisted methods using a statistical framework rely on a common set of extracted points located on the bone and soft-tissue surfaces. Most of the facial reconstruction methods then consist in predicting the position of the soft-tissue surface points, when the positions of the bone surface points are known. We propose to use Latent Root Regression for prediction. The results obtained are then compared to those given by Principal Components Analysis linear models. In conjunction, we have evaluated the influence of the number of skull landmarks used. Anatomical skull landmarks are completed iteratively by points located upon geodesics which link these anatomical landmarks, thus enabling us to artificially increase the number of skull points. Facial points are obtained using a mesh-matching algorithm between a common reference mesh and individual soft-tissue surface meshes. The proposed method is validated in term of accuracy, based on a leave-one-out cross-validation test applied to a homogeneous database. Accuracy measures are obtained by computing the distance between the original face surface and its reconstruction. Finally, these results are discussed referring to current computer-assisted reconstruction facial techniques.

Craniofacial reconstruction based on a hierarchical dense deformable model

“…Then the average absolute error of correspondent points for the global and hierarchical model is 1.65 and 1.50 mm. Comparing with the results in [12,15], the result is acceptable considering that some important properties, such as Body Mass Index (BMI), age, and gender, are not integrated into the model. The distribution of the average reconstruction error for every tests are shown as histogram in Figure 8.…”

“…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].…”

“…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.…”

A Synthetic Feature Skull Descriptor for 3D Skull Similarity Measurement

“…Some works are based on the statistical model methods to extract the features of skulls and faces [15][16][17][18]. Duan et al [17] proposed a skull identification method based on PCA that matches an unknown skull with enrolled 3D faces, in which the mapping between the skull and face is obtained using canonical correlation analysis.…”

“…Compute the SVD of WKDD wkdd svd(p) and wkdd svd(q) (12) Compute the CDF of WKDD wkdd cdf(p) and wkdd cdf(q) (13) SWKDD p ⟵ wkdd svd(p) * wkdd cdf(p) // computer the SWKDD p (14) SWKDD q ⟵ wkdd svd(q) * wkdd cdf(q) // computer the SWKDD q (15) Compute the skull similarity distance: D(skull p , skull q ) (16) end while ALGORITHM 1: SWKDD for skull similarity measure algorithm. 6 Mathematical Problems in Engineering subjective judgment.…”