Abstract
Pose variation is one key challenge in face recognition.
As opposed to current techniques for pose invariant face
recognition, which either directly extract pose invariant features for recognition, or first normalize profile face images
to frontal pose before feature extraction, we argue that it
is more desirable to perform both tasks jointly to allow
them to benefit from each other. To this end, we propose
a Pose Invariant Model (PIM) for face recognition in the
wild, with three distinct novelties. First, PIM is a novel
and unified deep architecture, containing a Face Frontalization sub-Net (FFN) and a Discriminative Learning sub-Net
(DLN), which are jointly learned from end to end. Second,
FFN is a well-designed dual-path Generative Adversarial Network (GAN) which simultaneously perceives global
structures and local details, incorporated with an unsupervised cross-domain adversarial training and a “learning
to learn” strategy for high-fidelity and identity-preserving
frontal view synthesis. Third, DLN is a generic Convolutional Neural Network (CNN) for face recognition with
our enforced cross-entropy optimization strategy for learning discriminative yet generalized feature representation.
Qualitative and quantitative experiments on both controlled
and in-the-wild benchmarks demonstrate the superiority of
the proposed model over the state-of-the-arts