A novel approach to 3D face recognition based on rigid region feature points is proposed to solve the problem of expression variance. The feature points of a face image are extracted on the face texture image by image block center vector sampling and probability map spatial relation model approximation

and the feature points in the nonrigid region are deleted. According to the serial number of the sampling points that are extracted from the face texture image

the 3D geometric information of the feature points of the face image is obtained based on the geometric information of the face space

and the subregion of the rigid region centered at the feature points is established. The subregion is used as the local feature for face recognition test. The contributions of different subregions to face recognition are obtained

and the result of face recognition is weighted by the contribution rate of different subregions. Experimental tests are performed on the FRGC ver2.0 3D face database. The recognition accuracy rate is 98.5%. The false accuracy rate is 0.001

and the verification rate is 99.2%. The method of non-neutral expression of 3D face recognition demonstrates good recognition performance. The proposed approach can effectively overcome the influence of facial expression variance on 3D face recognition because of the deleted feature points in the nonrigid region and has good robustness to the holes and sharp noises in the 3D data. This approach can greatly improve the performance of 3D face recognition.