Pairwise Relational Networks using Local Appearance Features for Face Recognition
This is an incremental improvement for face recognition systems, enhancing accuracy on standard benchmarks.
The paper tackles face recognition by proposing a pairwise relational network (PRN) that captures identity-dependent relations between local appearance features, achieving 99.76% accuracy on LFW and state-of-the-art 96.3% on YTF.
We propose a new face recognition method, called a pairwise relational network (PRN), which takes local appearance features around landmark points on the feature map, and captures unique pairwise relations with the same identity and discriminative pairwise relations between different identities. The PRN aims to determine facial part-relational structure from local appearance feature pairs. Because meaningful pairwise relations should be identity dependent, we add a face identity state feature, which obtains from the long short-term memory (LSTM) units network with the sequential local appearance features. To further improve accuracy, we combined the global appearance features with the pairwise relational feature. Experimental results on the LFW show that the PRN achieved 99.76% accuracy. On the YTF, PRN achieved the state-of-the-art accuracy (96.3%). The PRN also achieved comparable results to the state-of-the-art for both face verification and face identification tasks on the IJB-A and IJB-B. This work is already published on ECCV 2018.