In this thesis, we explore the statistical and geometrical behavior of uncontrolled parameters of human face, including both rigid transform caused by head pose and non-rigid transform caused by facial expression. We focus on developing 3D facial recognition schemes that can be robust for these uncontrolled parameters. This thesis presents a novel 3D face recognition method by means of the evolution of iso-geodesic distance curves. Specifically, the proposed method compares two neighboring iso-geodesic distance curves, and formalizes the evolution between them as a one-dimensional function, named evolution angle function, which is Euclidean invariant. The novelty of this paper consists in formalizing 3D face by an evolution angle functions, and in computing the distance between two faces by that of two functions. Experiments on Face Recognition Grand Challenge (FRGC) ver2.0 shows that our approach works very well on neutral faces. By introducing a weight function, we also show a promising result on non-neutral face database. A 3D surface segmentation scheme is developed to detect the partial similarity between facial images. The proposed algorithm is based on iterative closest point (ICP) algorithm, which uses mean square distance as the cost function and is not able to detect partial similarities. The presented thesis make an improvement of ICP algorithm by iteratively removing points contributing largest error, and the remaining area of surface can be shown to be the partial similarity between two surface