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 https://github.com/mk-minchul/AdaFace.

翻译：摘要：低质量人脸数据集中的识别具有挑战性，因为面部属性被遮挡或退化。基于边际损失函数的进展提升了嵌入空间中面部的可区分性。此外，先前研究探讨了自适应损失对错误分类（困难）样本赋予更大权重的效果。本文引入损失函数中另一个自适应维度，即图像质量。我们认为，针对错误分类样本的强调策略应根据其图像质量进行调整。具体而言，简单或困难样本的相对重要性应基于样本的图像质量。我们提出一种新损失函数，根据图像质量对不同难度的样本赋予差异化权重。该方法通过利用特征范数近似图像质量，以自适应边界函数的形式实现。大量实验表明，我们的方法AdaFace在四个数据集（IJB-B、IJB-C、IJB-S和TinyFace）上提升了人脸识别性能，超越了现有最先进方法（SoTA）。代码与模型已发布于https://github.com/mk-minchul/AdaFace。