论文标题
adaface：面部识别的质量自适应边距
AdaFace: Quality Adaptive Margin for Face Recognition
论文作者
论文摘要
低质量的面部数据集中的识别是具有挑战性的，因为面部属性被遮盖和退化。基于保证金的损失功能的进步导致嵌入空间中面部的可区分性增强。此外，先前的研究研究了自适应损失的效果，以使错误分类（硬）示例更为重要。在这项工作中，我们介绍了损失功能的适应性的另一个方面，即图像质量。我们认为，应根据其图像质量进行调整来强调错误分类样本的策略。具体而言，简单或硬样品的相对重要性应基于样本的图像质量。我们提出了一个新的损失函数，该功能根据其图像质量强调不同困难的样本。我们的方法通过使用特征规范近似图像质量来以自适应边缘函数的形式实现这一目标。广泛的实验表明，我们的方法ADAFAFE提高了四个数据集（IJB-B，IJB-C，IJB-S和TinyFace）上最先进（SOTA）的面部识别性能。代码和模型在https://github.com/mk-minchul/adaface中发布。
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.
