Collaborative representation based face recognition using a hybrid similarity measure with single training sample per person

This paper proposes a Collaborative Representation (CR) based face recognition strategy, wherein a hybrid similarity measure has been used for the computation of the reconstruction residual. In this work Single Sample per Person (SSPP) methodology is used for creating the training dataset. The hybrid similarity measure proposed for computation of the reconstruction residual is a composite formulation of the Procrustes Similarity analysis along with the conventional Euclidean Distance metric. The proposed method is implemented for several benchmark face databases and the results obtained demonstrate the substantial improvement achieved in the recognition rates, in comparison to the known Collaborative Representation (CR) based face recognition strategy.

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