In this paper, we propose a discriminative aggregation network (DAN) for video face
recognition, which aims to integrate information from video frames effectively and efficiently.
Different from existing aggregation methods, our method aggregates raw video frames
directly instead of the features obtained by complex processing. By combining the idea of
metric learning and adversarial learning, we learn an aggregation network that produces
more discriminative synthesized images compared to input frames. Our framework reduces …

In this paper, we propose a discriminative aggregation network method for video-based face
recognition and person re-identification, which aims to integrate information from video
frames for feature representation effectively and efficiently. Unlike existing video aggregation
methods, our method aggregates raw video frames directly instead of the features obtained
by complex processing. By combining the idea of metric learning and adversarial learning,
we learn an aggregation network to generate more discriminative images compared to the …