  In recent years, face recognition systems have achieved exceptional success
due to promising advances in deep learning architectures. However, they still
fail to achieve expected accuracy when matching profile images against a
gallery of frontal images. Current approaches either perform pose normalization
(i.e., frontalization) or disentangle pose information for face recognition. We
instead propose a new approach to utilize pose as an auxiliary information via
an attention mechanism. In this paper, we hypothesize that pose attended
information using an attention mechanism can guide contextual and distinctive
feature extraction from profile faces, which further benefits a better
representation learning in an embedded domain. To achieve this, first, we
design a unified coupled profile-to-frontal face recognition network. It learns
the mapping from faces to a compact embedding subspace via a class-specific
contrastive loss. Second, we develop a novel pose attention block (PAB) to
specially guide the pose-agnostic feature extraction from profile faces. To be
more specific, PAB is designed to explicitly help the network to focus on
important features along both channel and spatial dimension while learning
discriminative yet pose invariant features in an embedding subspace. To
validate the effectiveness of our proposed method, we conduct experiments on
both controlled and in the wild benchmarks including Multi-PIE, CFP, IJBC, and
show superiority over the state of the arts.
