FR-ANet: A Face Recognition Guided Facial Attribute Classification Network

Jiajiong Cao, Yingming Li, Xi Li, Zhongfei Zhang

AAAI 2018 pp. 8057-8058

doi:10.1609/AAAI.V32I1.12175 /aaai/2018/cao2018aaai-fr/

Abstract

In this paper, we study the problem of facial attribute learning. In particular, we propose a Face Recognition guided facial Attribute classification Network, called FR-ANet. All the attributes share low-level features, while high-level features are specially learned for attribute groups. Further, to utilize the identity information, high-level features are merged to perform face identity recognition. The experimental results on CelebA and LFWA datasets demonstrate the promise of the FR-ANet.

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Cite

Text

Cao et al. "FR-ANet: A Face Recognition Guided Facial Attribute Classification Network." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.12175

Markdown

[Cao et al. "FR-ANet: A Face Recognition Guided Facial Attribute Classification Network." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/cao2018aaai-fr/) doi:10.1609/AAAI.V32I1.12175

BibTeX

@inproceedings{cao2018aaai-fr,
  title     = {{FR-ANet: A Face Recognition Guided Facial Attribute Classification Network}},
  author    = {Cao, Jiajiong and Li, Yingming and Li, Xi and Zhang, Zhongfei},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {8057-8058},
  doi       = {10.1609/AAAI.V32I1.12175},
  url       = {https://mlanthology.org/aaai/2018/cao2018aaai-fr/}
}