SFS based neural algorithm for robust 3D face shape recovery robust read robust

摘要

A novel approach to the recovery of 3D shape information from 2D images is described. The proposed approach is based on using the neural networks, as a parametric representation of the three-dimensional object and the shape from shading problem is formulated as the minimization of an intensity error function with respect to the network weights. We measure the average absolute percentage error per pixel (AAPEPP) for each recovered face part. The new algorithms for data driven, stable, update the surface slope and height maps are proposed. This approach significantly reduces the residual errors. A description of simulations of the implemented architecture and the quite satisfactory experimental results are reported as well as the comparisons with some classic approaches to the problem.