  For the past decades, face recognition (FR) has been actively studied in
computer vision and pattern recognition society. Recently, due to the advances
in deep learning, the FR technology shows high performance for most of the
benchmark datasets. However, when the FR algorithm is applied to a real-world
scenario, the performance has been known to be still unsatisfactory. This is
mainly attributed to the mismatch between training and testing sets. Among such
mismatches, face misalignment between training and testing faces is one of the
factors that hinder successful FR. To address this limitation, we propose a
face shape-guided deep feature alignment framework for FR robust to the face
misalignment. Based on a face shape prior (e.g., face keypoints), we train the
proposed deep network by introducing alignment processes, i.e., pixel and
feature alignments, between well-aligned and misaligned face images. Through
the pixel alignment process that decodes the aggregated feature extracted from
a face image and face shape prior, we add the auxiliary task to reconstruct the
well-aligned face image. Since the aggregated features are linked to the face
feature extraction network as a guide via the feature alignment process, we
train the robust face feature to the face misalignment. Even if the face shape
estimation is required in the training stage, the additional face alignment
process, which is usually incorporated in the conventional FR pipeline, is not
necessarily needed in the testing phase. Through the comparative experiments,
we validate the effectiveness of the proposed method for the face misalignment
with the FR datasets.
