In this paper we present a novel craniofacial reconstruction method employing statistical tissue thickness information. The tissue thickness data gotten from CT images are represented as 2D tissue maps. The input (target) skull model is parameterized onto a 2D planar map and the landmarks are utilized to train a RBFN (radial basis function network), which realizes warping of planar maps between the target tissue and the generic tissue. The generic tissue is aligned onto the target skull by applying the trained network onto it, and thus the target facial map can be obtained by a simple addition of the warped generic maps. Finally, we interactively deform the model based on a RBFN to make the facial meshes more personalized, and map the texture from orthogonal photos onto the reconstructed model to improve rendering effects. Experiment results show that the proposed approach is helpful in improving the recognition ability in forensic applications.
['Yuru Pei', 'Hongbin Zha', 'Zhongbiao Yuan']
Tissue map based craniofacial reconstruction and facial deformation using RBF network