Abstract

In this paper we test a range of three-dimensional face recognition systems, based on the fishersurface method developed in previous work. We show the effect of using a variety of facial surface representations and suggest a method of identifying and extracting useful qualities offered by each system. Combing these components into a unified surface subspace, we create a three dimensionalface recognition system producing significantly lower error rates than individual systems tested on the same data. We evaluate systems by performing up to 1,079,715 verification operations on a large test set of 3D face models. Results are presented in the form of false acceptance and false rejection rates, generated by varying a decision threshold applied to a distance metric in combined surface space.