Reconstruction-Based Disentanglement for Pose-Invariant Face Recognition
ICCV 2017 | Generic data-driven deep face features might confound images of the same identity under large poses with other identities. We propose a feature reconstruction metric learning to disentangle identity and pose information in the latent feature space. The disentangled feature space encourages identity features of the same subject to be clustered together in spite of pose variation. Experiments on both controlled and in-the-wild face datasets show that our method consistently outperforms the state-of-the-art, especially on images with large head-pose variations.
Collaborators: Xi Peng, Kihyuk Sohn, Dimitris N. Metaxas, Manmohan Chandraker