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We propose a new face recognition method, called a pairwise relationalnetwork (PRN), which takes local appearance features around landmark points onthe feature map, and captures unique pairwise relations with the same identityand discriminative pairwise relations between different identities. The PRNaims to determine facial part-relational structure from local appearancefeature pairs. Because meaningful pairwise relations should be identitydependent, we add a face identity state feature, which obtains from the longshort-term memory (LSTM) units network with the sequential local appearancefeatures. To further improve accuracy, we combined the global appearancefeatures with the pairwise relational feature. Experimental results on the LFWshow that the PRN achieved 99.76% accuracy. On the YTF, PRN achieved thestate-of-the-art accuracy (96.3%). The PRN also achieved comparable results tothe state-of-the-art for both face verification and face identification taskson the IJB-A and IJB-B. This work is already published on ECCV 2018.

Pairwise Relational Networks using Local Appearance Features for Face Recognition