Recognition in low quality face datasets is challenging because facial
attributes are obscured and degraded. Advances in margin-based loss functions
have resulted in enhanced discriminability of faces in the embedding space.
Further, previous studies have studied the effect of adaptive losses to assign
more importance to misclassified (hard) examples. In this work, we introduce
another aspect of adaptiveness in the loss function, namely the image quality.
We argue that the strategy to emphasize misclassified samples should be
adjusted according to their image quality. Specifically, the relative
importance of easy or hard samples should be based on the sample's image
quality. We propose a new loss function that emphasizes samples of different
difficulties based on their image quality. Our method achieves this in the form
of an adaptive margin function by approximating the image quality with feature
norms. Extensive experiments show that our method, AdaFace, improves the face
recognition performance over the state-of-the-art (SoTA) on four datasets
(IJB-B, IJB-C, IJB-S and TinyFace). Code and models are released in
this https URL

本研究提出了一种新的损失函数 AdaFace，它可以基于图像质量加重不同难度的样本，通过用特征范数来近似图像质量，从而提高人脸识别性能。

AdaFace：用于人脸识别的质量自适应边距

AdaFace: Quality Adaptive Margin for Face Recognition

Face recognition has witnessed significant progresses due to the advances of
deep convolutional neural networks (CNNs), the central challenge of which, is
feature discrimination. To address it, one group tries to exploit mining-based
strategies (\textit{e.g.}, hard example mining and focal loss) to focus on the
informative examples. The other group devotes to designing margin-based loss
functions (\textit{e.g.}, angular, additive and additive angular margins) to
increase the feature margin from the perspective of ground truth class. Both of
them have been well-verified to learn discriminative features. However, they
suffer from either the ambiguity of hard examples or the lack of discriminative
power of other classes. In this paper, we design a novel loss function, namely
support vector guided softmax loss (SV-Softmax), which adaptively emphasizes
the mis-classified points (support vectors) to guide the discriminative
features learning. So the developed SV-Softmax loss is able to eliminate the
ambiguity of hard examples as well as absorb the discriminative power of other
classes, and thus results in more discrimiantive features. To the best of our
knowledge, this is the first attempt to inherit the advantages of mining-based
and margin-based losses into one framework. Experimental results on several
benchmarks have demonstrated the effectiveness of our approach over
state-of-the-arts.

提出了一种新颖的损失函数 (SV-Softmax), 该函数从支持向量的角度来指导深度卷积神经网络的学习，使得因难例引起的歧义和其他类别的歧义都得以解决，产生了更具区分性的特征.