Abstract
This paper proposes learning disentangled but complementary face features with a minimal supervision by face
identification. Specifically, we construct an identity Distilling and Dispelling Autoencoder (D2AE) framework that
adversarially learns the identity-distilled features for identity verification and the identity-dispelled features to fool
the verification system. Thanks to the design of two-stream
cues, the learned disentangled features represent not only
the identity or attribute but the complete input image. Comprehensive evaluations further demonstrate that the proposed features not only preserve state-of-the-art identity
verification performance on LFW, but also acquire comparable discriminative power for face attribute recognition on
CelebA and LFWA. Moreover, the proposed system is ready
to semantically control the face generation/editing based on
various identities and attributes in an unsupervised manner