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. In this paper, we propose a novel Attention-Set based Metric Learning (ASML) method for VFR. It is a promising and generalized extension of Maximum Mean Discrepancy with Memory Attention Weighting inspired by Neural Turing Machine. ASML can be naturally integrated into Convolutional Neural Networks, resulting in an end-to-end learning scheme. Our method achieves state-of-the-art performance for the task of video face recognition on three widely used benchmarks including YouTubeFace, YouTube Celebrities and Celebrity-1000.