3D Face Recognition in Continuous Spaces

This work introduces a new approach for face recognition based on 3D scans. The main idea of the proposed method is that of converting the 3D face scans into a functional representation, performing all the subsequent processing in the continuous space. To this end, a model alignment problem is first solved by combining graph matching and clustering. Fiducial points of the face are initially detected by analysis of continuous functions computed on the surface. Then, the alignment is performed by transforming the geometric graphs whose nodes are the critical points of the representative function of the surface in previously determined subspaces. A clustering step is finally applied to correct small displacement in the models. The 3D face representation is then obtained on the aligned models by functions carefully selected according to mathematical and computational criteria. In particular, the face is divided into regions, which are treated as independent domains where a set of functions is determined by fitting the surface data using the least squares method. Experimental results demonstrate the feasibility of the method.