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Pose-invariant face recognition refers to the problem of identifying orverifying a person by analyzing face images captured from different poses. Thisproblem is challenging due to the large variation of pose, illumination andfacial expression. A promising approach to deal with pose variation is tofulfill incomplete UV maps extracted from in-the-wild faces, then attach thecompleted UV map to a fitted 3D mesh and finally generate different 2D faces ofarbitrary poses. The synthesized faces increase the pose variation for trainingdeep face recognition models and reduce the pose discrepancy during the testingphase. In this paper, we propose a novel generative model called AttentionResCUNet-GAN to improve the UV map completion. We enhance the original UV-GANby using a couple of U-Nets. Particularly, the skip connections within eachU-Net are boosted by attention gates. Meanwhile, the features from two U-Netsare fused with trainable scalar weights. The experiments on the popularbenchmarks, including Multi-PIE, LFW, CPLWF and CFP datasets, show that theproposed method yields superior performance compared to other existing methods.

Facial UV Map Completion for Pose-invariant Face Recognition: A Novel Adversarial Approach based on Coupled Attention Residual UNets