A two-step matching method for 3D face recognition is proposed. Feature points are detected based on curvature and geometric constraint. Then the symmetrical plane of 3D face is determined based on the feature points

and the profile is determined by the obtained symmetrical plane. The profile is used to form a rejection classifier

which quickly eliminates a large number of candidate faces at an early stage for an efficient recognition in case of large galleries. The remaining faces are then verified using a novel region-based matching approach

which is robust to facial expressions. This approach automatically segments the rigid regions

which are relatively less sensitive to expressions and it matches them separately using a modified iterative closest point (ICP) algorithm. The results of all the matching engines are fused using a sum rule to achieve higher accuracy. Our simulation experiment on 3D_RMA database demonstrates that the proposed method is simple

efficient and robust.