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