An enhanced subspace method for face recognition

In this paper we introduce a new face recognition approach based on the representation of each individual by several lower dimensional subspaces obtained by an unsupervised clustering of different poses: this provides a higher robustness to face variations than traditional subspace approaches. A set of subspaces is created for each individual, starting from a feature vector extracted through a bank of Gabor filters and non-linear Fisher transform. Extensive experiments carried out on the FERET database of faces, which is the most common benchmark in this area, prove the advantages of the proposed approach when compared with other well-known techniques. These results confirm the robustness of our approach against appearance variations due to expression, illumination and pose changes or to aging effects.