Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition

@article{Zhan2018ConsensusDrivenPI,
  title={Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition},
  author={Xiaohang Zhan and Ziwei Liu and Junjie Yan and Dahua Lin and Chen Change Loy},
  journal={ArXiv},
  year={2018},
  volume={abs/1809.01407}
}
Face recognition has witnessed great progress in recent years, mainly attributed to the high-capacity model designed and the abundant labeled data collected. However, it becomes more and more prohibitive to scale up the current million-level identity annotations. In this work, we show that unlabeled face data can be as effective as the labeled ones. Here, we consider a setting closely mimicking the real-world scenario, where the unlabeled data are collected from unconstrained environments and… 
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55 Citations
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55 Citations

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  • Qi ZhangZhen LeiS. Li
  • Computer Science
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Asymmetric Rejection Loss for Fairer Face Recognition

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On Mitigating Hard Clusters for Face Clustering

Two novel modules are introduced, Neighborhood-Diffusion-based Density (NDDe) and Transition-Probability-based Distance (TPDi), based on which the standard Density Peak Clustering algorithm can be applied with a uniform threshold, to tackle the problem of identifying small or sparse face image clusters.

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