论文标题
数据不确定性学习面部识别
Data Uncertainty Learning in Face Recognition
论文作者
论文摘要
建模数据不确定性对于嘈杂的图像很重要，但很少探索以识别面部。先锋工作，PFE，通过将嵌入每个面部图像作为高斯分布进行建模来考虑不确定性。这很有效。但是，它使用现有模型的固定功能（平均值）。它仅估计差异并依赖于临时且昂贵的度量。因此，它并不容易使用。目前尚不清楚不确定性如何影响特征学习。 这项工作将数据不确定性学习以面对识别，以便第一次同时学习功能（均值）和不确定性（差异）。提出了两种学习方法。它们易于使用，胜过现有的确定性方法，以及在挑战不受约束的情况下进行的PFE。我们还提供有关纳入不确定性估计如何有助于减少嘈杂样本的不良影响并影响特征学习的洞察力分析。
Modeling data uncertainty is important for noisy images, but seldom explored for face recognition. The pioneer work, PFE, considers uncertainty by modeling each face image embedding as a Gaussian distribution. It is quite effective. However, it uses fixed feature (mean of the Gaussian) from an existing model. It only estimates the variance and relies on an ad-hoc and costly metric. Thus, it is not easy to use. It is unclear how uncertainty affects feature learning. This work applies data uncertainty learning to face recognition, such that the feature (mean) and uncertainty (variance) are learnt simultaneously, for the first time. Two learning methods are proposed. They are easy to use and outperform existing deterministic methods as well as PFE on challenging unconstrained scenarios. We also provide insightful analysis on how incorporating uncertainty estimation helps reducing the adverse effects of noisy samples and affects the feature learning.
