  Despite the great success achieved by deep learning methods in face
recognition, severe performance drops are observed for large pose variations in
unconstrained environments (e.g., in cases of surveillance and photo-tagging).
To address it, current methods either deploy pose-specific models or frontalize
faces by additional modules. Still, they ignore the fact that identity
information should be consistent across poses and are not realizing the data
imbalance between frontal and profile face images during training. In this
paper, we propose an efficient PoseFace framework which utilizes the facial
landmarks to disentangle the pose-invariant features and exploits a
pose-adaptive loss to handle the imbalance issue adaptively. Extensive
experimental results on the benchmarks of Multi-PIE, CFP, CPLFW and IJB have
demonstrated the superiority of our method over the state-of-the-arts.
