Deformable surface fitting methods have been widely used to establish dense correspondence across different 3D objects of the same class. Dense correspondence is a critical step in constructing morphable face models for face recognition. In this paper a mainstream method for constructing dense correspondences is evaluated on 912 3D face scans from the Face Recognition Grand Challenge FRGC V1 database. A number of modifications to the standard deformable surface approach are introduced to overcome limitations identified in the evaluation. Proposed modifications include multi-resolution fitting, adaptive correspondence search range and enforcing symmetry constraints. The modified deformable surface approach is validated on the 912 FRGC 3D face scans and is shown to overcome limitations of the standard approach which resulted in gross fitting errors. The modified approach halves the rms fitting error with 98% of points within 0.5mm of their true position compared to 67% with the standard approach.