Exploring Disentangled Feature Representation Beyond Face Identification

@article{Liu2018ExploringDF,
  title={Exploring Disentangled Feature Representation Beyond Face Identification},
  author={Yu Liu and Fangyin Wei and Jing Shao and Lu Sheng and Junjie Yan and Xiaogang Wang},
  journal={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2018},
  pages={2080-2089}
}
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… CONTINUE READING

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Key Quantitative Results

  • The proposed approach is compared both quantitatively and qualitatively with state of the arts, achieving 1) accuracy of 99.80% on face verification benchmark LFW[12], 2) remarkable performance on attribute classification benchmarks LFWA[26] & CelebA[26], and 3) superior capability on various generative tasks such as semantic face editing.

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