Fully Associative Patch-based 1-to-N Matcher for Face Recognition
This work addresses face recognition accuracy for security or identification applications, but it is incremental as it builds upon an existing system with specific gains.
The paper tackled face recognition by proposing a Fully Associative Patch-based Signature Matcher (FAPSM) that learns correlations between facial patches to improve matching accuracy, resulting in Rank-1 accuracy improvements of 3% on UHDB31 and 0.55% on IJB-A datasets compared to the baseline UR2D system.
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.