We addressed an important issue for the problem of non-rigid registration. We proposed the Feature-Driven framework, relaxing the groupwise requirement for using efficient compositional algorithms such Inverse Compositional and Learning-Based algorithms. We also explained in details the Feature-Driven parameterization for the TPS and the FFD warps. Experiments show that Feature-Driven algorithms are more efficient compared to classical ones with additive update of the parameters. Overall, the best algorithm is the combination of the Feature-Driven framework, the Forward Compositional update of the parameters and the local registration based on Learning. The proposed algorithms make foreseeable accurate real-time surface registration.