In the field of public safety surveillance, suspects may disguise themselves by painting their faces, challenging the face recognition system. The well-developed ordinary, two-dimensional image face recognition methods rely mainly on the texture features of the face images, which leaves the traditional face recognition system relatively vulnerable to the camouflage attacks. We set our sights on three-dimensional (3D) alternatives. A 3D facial recognition method is proposed which extracts 3D features of each individual that are not related to face texture. Considering actual requirements, we applied binocular stereo matching to obtain 3D face point clouds. Hereafter, we included a spatial representation for classification and recognition of 3D face, based on which we then adopted a multicascade classifier. By reconstructing, extracting features, and identifying multiple 3D faces, comparison experiment results demonstrate that our proposed method discerned 3D faces correctly, defeated the face camouflage attacks effectively, and showed promising application prospects for public safety surveillance.