Seeing through the Mask: Multi-task Generative Mask Decoupling Face
  Recognition
Zhaohui Wang Sufang Zhang Jianteng Peng Xinyi Wang Yandong Guo
The outbreak of COVID-19 pandemic make people wear masks more frequently than
ever. Current general face recognition system suffers from serious performance
degradation,when encountering occluded scenes. The potential reason is that
face features are corrupted by occlusions on key facial regions. To tackle this
problem, previous works either extract identity-related embeddings on feature
level by additional mask prediction, or restore the occluded facial part by
generative models. However, the former lacks visual results for model
interpretation, while the latter suffers from artifacts which may affect
downstream recognition. Therefore, this paper proposes a Multi-task gEnerative
mask dEcoupling face Recognition (MEER) network to jointly handle these two
tasks, which can learn occlusionirrelevant and identity-related representation
while achieving unmasked face synthesis. We first present a novel mask
decoupling module to disentangle mask and identity information, which makes the
network obtain purer identity features from visible facial components. Then, an
unmasked face is restored by a joint-training strategy, which will be further
used to refine the recognition network with an id-preserving loss. Experiments
on masked face recognition under realistic and synthetic occlusions benchmarks
demonstrate that the MEER can outperform the state-ofthe-art methods.