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

Authors

  • Jiajiong Cao Zhejiang University
  • Yingming Li Zhejiang University
  • Xi Li Zhejiang University
  • Zhongfei Zhang Zhejiang University

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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Published

2018-04-29

How to Cite

Cao, J., Li, Y., Li, X., & Zhang, Z. (2018). FR-ANet: A Face Recognition Guided Facial Attribute Classification Network. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/12175