Learning Discriminative Aggregation Network for Video-Based Face Recognition and Person Re-identification

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 raw input frames. Our framework reduces the number of image frames per video to be processed and significantly speeds up the recognition procedure. Furthermore, low-quality frames containing misleading information can be well filtered and denoised during the aggregation procedure, which makes our method more robust and discriminative. Experimental results on several widely used datasets show that our method can generate discriminative images from video clips and improve the overall recognition performance in both the speed and the accuracy for video-based face recognition and person re-identification.