Bayesian estimation of optimal craniofacial reconstructions

Forensic craniofacial reconstruction (CFR) aims at estimating the facial outlook associated to an unknown skull for victim identification. Computerized CFR techniques are essentially a virtual mimicking of manual CFR techniques and all share the same conceptual model-based framework. We propose a fully automated Bayesian based statistical framework estimating the most probable face, according to a known craniofacial model (CFM), given the, possibly inaccurate, skull data. A multivariate Gaussian distribution is assumed for the shape parameters of the CFM, only allowing face-like solutions. The CFM is improved by encoding tissue depth differently as an extra value for 52 landmarks on the face and by incorporating gray-valued texture information. A fully automated and consistent technique is obtained by the use of an implicit target skull representation (TSR). The most plausible face associated to the target skull is calculated using an expectation-maximization procedure that is robust to small (noise) and/or gross errors (outliers). A clinical database of 12 individuals is used for simulating realistic reconstruction scenarios. Validation is performed in terms of reconstruction accuracy and recognition success. Within the same EM reconstruction framework, the proposed procedure is compared to alternative reconstructions using different target skull representations and different CFMs incorporating various amounts of covariance. The results indicate that the proposed CFM performs better than the other models. Furthermore, the use of the implicit TSR generates more consistent and better results compared to a realistic landmark based skull representation. Finally, these results also confirm that the Bayesian framework formulation is indeed robust against noise and outliers in the skull data.