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This paper focuses on improving face recognition performance by a patch-based1-to-N signature matcher that learns correlations between different facialpatches. A Fully Associative Patch-based Signature Matcher (FAPSM) is proposedso that the local matching identity of each patch contributes to the globalmatching identities of all the patches. The proposed matcher consists of threesteps. First, based on the signature, the local matching identity and thecorresponding matching score of each patch are computed. Then, a fullyassociative weight matrix is learned to obtain the global matching identitiesand scores of all the patches. At last, the l1-regularized weighting is appliedto combine the global matching identity of each patch and obtain a finalmatching identity. The proposed matcher has been integrated with the UR2Dsystem for evaluation. The experimental results indicate that the proposedmatcher achieves better performance than the current UR2D system. The Rank-1accuracy is improved significantly by 3% and 0.55% on the UHDB31 dataset andthe IJB-A dataset, respectively.

Fully Associative Patch-based 1-to-N Matcher for Face Recognition