Subspace adaptation for incremental face learning

This paper introduces a new face recognition approach that allows face variations (produced by aging and other appearance changes) to be dealt with. During the initial learning, a set of MKL subspaces is created for each individual, starting from the feature vectors extracted through a bank of Gabor filters. Then, during the normal system operation, an incremental updating technique can be applied to adjust the subspaces without recalculating the face models from scratch; this makes the method able to cope with gradual changes that occur over time. The results of the experimentation performed on three face databases prove the advantages of the proposed approach with respect to other well-known techniques; in particular, our method achieves better accuracy and higher robustness against face variations.