Attention-Set based Metric Learning for Video Face Recognition
  Face recognition has made great progress with the development of deep
learning. However, video face recognition (VFR) is still an ongoing task due to
various illumination, low-resolution, pose variations and motion blur. Most
existing CNN-based VFR methods only obtain a feature vector from a single image
and simply aggregate the features in a video, which less consider the
correlations of face images in one video. In this paper, we propose a novel
Attention-Set based Metric Learning (ASML) method to measure the statistical
characteristics of image sets. It is a promising and generalized extension of
Maximum Mean Discrepancy with memory attention weighting. First, we define an
effective distance metric on image sets, which explicitly minimizes the
intra-set distance and maximizes the inter-set distance simultaneously. Second,
inspired by Neural Turing Machine, a Memory Attention Weighting is proposed to
adapt set-aware global contents. Then ASML is naturally integrated into CNNs,
resulting in an end-to-end learning scheme. Our method achieves
state-of-the-art performance for the task of video face recognition on the
three widely used benchmarks including YouTubeFace, YouTube Celebrities and
Celebrity-1000.
