Abstract: Existing subspace learning-based face recognition methods assume the same loss from all misclassifications. In the real-world face recognition applications, however, different misclassifications can lead to different losses. Motivated by this concern, a cost-sensitive supervised manifold learning approach for face recognition was proposed. The proposed approach incorporated a cost matrix to specify the different costs associated with misclassifications of subjects, into locality preserving projection algorithm, which devised the corresponding cost-sensitive methods, namely, cost-sensitive locality preserving projections (Cos-Sen LPP), to achieve a minimal overall loss. Three face databases were put into the experiments and experimental results show that Cos-Sen LPP method can achieve minimal cost than existing subspace learning-based face recognition methods.

Key words: cost-sensitive, manifold learning, face recognition, Locality Preserving Projections (LPP)