1 research outputs found
Reliable Probabilistic Face Embeddings in the Wild
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. Then, an output-constraint loss is proposed to penalize the
variance of the uncertainty output, which can regularize the output of the
neural network. In addition, an identification preserving loss is proposed to
improve the discriminative of the MLS metric, and a multi-layer feature fusion
module is proposed to improve the neural network's uncertainty estimation
ability. Comprehensive experiments show that the proposed method can achieve
comparable or better results in 8 benchmarks than the state-of-the-art methods,
and can improve the performance of risk-controlled face recognition. The code
of ProbFace is publicly available in GitHub
(https://github.com/KaenChan/ProbFace).Comment: 14 page