Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Probabilistic Face Embeddings   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. 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 values. Probabilistic solutions
can then be naturally derived for matching and fusing PFEs using the
uncertainty information. Empirical evaluation on different baseline models,
training datasets and benchmarks show that the proposed method can improve the
face recognition performance of deterministic embeddings by converting them
into PFEs. The uncertainties estimated by PFEs also serve as good indicators of
the potential matching accuracy, which are important for a risk-controlled
recognition system.

11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int