Fast and Reliable Probabilistic Face Embeddings in the Wild

Understand

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 simplified to speedup the matching of face feature pairs.