  This paper aims to learn a compact representation of a video for video face
recognition task. We make the following contributions: first, we propose a meta
attention-based aggregation scheme which adaptively and fine-grained weighs the
feature along each feature dimension among all frames to form a compact and
discriminative representation. It makes the best to exploit the valuable or
discriminative part of each frame to promote the performance of face
recognition, without discarding or despising low quality frames as usual
methods do. Second, we build a feature aggregation network comprised of a
feature embedding module and a feature aggregation module. The embedding module
is a convolutional neural network used to extract a feature vector from a face
image, while the aggregation module consists of cascaded two meta attention
blocks which adaptively aggregate the feature vectors into a single
fixed-length representation. The network can deal with arbitrary number of
frames, and is insensitive to frame order. Third, we validate the performance
of proposed aggregation scheme. Experiments on publicly available datasets,
such as YouTube face dataset and IJB-A dataset, show the effectiveness of our
method, and it achieves competitive performances on both the verification and
identification protocols.
