Probabilistic Face Embeddings

ICCV 2019 Yichun ShiAnil K. Jain

Embedding methods have achieved success in face recognition by comparing facial features in a latent semantic space. However, in a fully unconstrained face setting, the facial features learned by the embedding model could be ambiguous or may not even be present in the input face, leading to noisy representations... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Face Verification IJB-A PFEfuse + match TAR @ FAR=0.01 97.5% # 2
TAR @ FAR=0.001 95.25 # 1
Face Verification IJB-C PFEfuse + match TAR @ FAR=0.01 97.17% # 1
TAR @ FAR=0.001 95.49 # 1
Face Verification Labeled Faces in the Wild PFEfuse+match Accuracy 99.82% # 3
Face Verification MegaFace PFEfuse + match Accuracy 92.51% # 5
Face Verification YouTube Faces DB PFEfuse+match Accuracy 97.36% # 4

Methods used in the Paper


METHOD TYPE
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