  Data uncertainty is commonly observed in the images for face recognition
(FR). However, deep learning algorithms often make predictions with high
confidence even for uncertain or irrelevant inputs. Intuitively, FR algorithms
can benefit from both the estimation of uncertainty and the detection of
out-of-distribution (OOD) samples. Taking a probabilistic view of the current
classification model, the temperature scalar is exactly the scale of
uncertainty noise implicitly added in the softmax function. Meanwhile, the
uncertainty of images in a dataset should follow a prior distribution. Based on
the observation, a unified framework for uncertainty modeling and FR, Random
Temperature Scaling (RTS), is proposed to learn a reliable FR algorithm. The
benefits of RTS are two-fold. (1) In the training phase, it can adjust the
learning strength of clean and noisy samples for stability and accuracy. (2) In
the test phase, it can provide a score of confidence to detect uncertain,
low-quality and even OOD samples, without training on extra labels. Extensive
experiments on FR benchmarks demonstrate that the magnitude of variance in RTS,
which serves as an OOD detection metric, is closely related to the uncertainty
of the input image. RTS can achieve top performance on both the FR and OOD
detection tasks. Moreover, the model trained with RTS can perform robustly on
datasets with noise. The proposed module is light-weight and only adds
negligible computation cost to the model.
