Corpus ID: 235593431

Fast and Reliable Probabilistic Face Embeddings in the Wild

@inproceedings{Chen2021FastAR,
  title={Fast and Reliable Probabilistic Face Embeddings in the Wild},
  author={Kai Chen and Qi Lv and Taihe Yi},
  year={2021}
}
  • Kai Chen, Qi Lv, Taihe Yi
  • Published 8 February 2021
  • Computer Science
Probabilistic Face Embeddings (PFE) can improve face recognition performance in unconstrained scenarios by integrating data uncertainty into the feature representation. However, existing PFE methods tend to be over-confident in estimating uncertainty and is too slow to apply to large-scale face matching. This paper proposes a regularized probabilistic face embedding method to improve the robustness and speed of PFE. Specifically, the mutual likelihood score (MLS) metric used in PFE is… Expand
An Integration of Deep Network with Random Forests Framework for Image Quality Assessment in Real-Time
In recent years, data providers are generating and streaming a large number of images. More particularly, processing images that contain faces have received great attention due to its numerousExpand

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