Application of semantic features in face recognition

We propose a new face recognition strategy, which integrates the extraction of semantic features from faces with tensor subspace analysis. The semantic features consist of the eyes and mouth, plus the region outlined by the centers of the three components. A new objective function is generated to fuse the semantic and tensor models for finding similarity between a face and its counterpart in the database. Furthermore, singular value decomposition is used to solve the eigenvector problem in the tensor subspace analysis and to project the geometrical properties to the face manifold. Experimental results demonstrate that the proposed semantic feature-based face recognition algorithm has favorable performance with more accurate convergence and less computational efforts.