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.

中文翻译：

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用于基于视频的人脸识别和人员重新识别的学习判别聚合网络

在本文中，我们提出了一种用于基于视频的人脸识别和人员重新识别的判别聚合网络方法，旨在有效地整合来自视频帧的信息以进行特征表示。与现有的视频聚合方法不同，我们的方法直接聚合原始视频帧，而不是通过复杂处理获得的特征。通过结合度量学习和对抗性学习的思想，我们学习了一个聚合网络，以生成与原始输入帧相比更具辨别力的图像。我们的框架减少了要处理的每个视频的图像帧数，并显着加快了识别过程。此外，在聚合过程中可以很好地过滤和去噪包含误导信息的低质量帧，这使我们的方法更加健壮和有辨别力。在几个广泛使用的数据集上的实验结果表明，我们的方法可以从视频片段中生成有辨别力的图像，并提高基于视频的人脸识别和人员重新识别的速度和准确度的整体识别性能。