Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos
ICCV 2017 | Despite rapid advances in face recognition, there remains a clear gap between the performance of still-image-based face recognition and video-based face recognition. To address this, we propose an image-to-video feature-level domain adaptation method to learn discriminative video-frame representations. It is achieved by distilling knowledge from the network to a video adaptation network, performing feature restoration through synthetic data augmentation and learning a domain-invariant feature through a domain-adversarial discriminator. Experiments on YouTube Faces and IJB-A demonstrate that our method achieves state-of-the-art accuracy in terms of video-based face recognition.
Collaborators: Kihyuk Sohn, Sifei Liu, Guangyu Zhong, Ming-Hsuan Yang, Manmohan Chandraker