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Clip: Probabilistic Face Embeddings Yichun Shi and Anil K. Jain Michigan State University, East Lansing, MI shiyichu@msu.edu, jain@cse.msu.edu Abstract Embedding methods have achieved success in face recognition by comparing facial features in a latent seman- tic 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. We propose Probabilistic Face Embeddings (PFEs), which represent each face image as a Gaussian distribution in the latent space. The mean of the distribution estimates the most likely feature values while the variance shows the uncertainty in the feature val-

Filename: 1904.09658.pdf

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