论文标题
八度损失：使面部识别可靠地分辨率
Octuplet Loss: Make Face Recognition Robust to Image Resolution
论文作者
论文摘要
图像分辨率或一般图像质量在当今面部识别系统的性能中起着至关重要的作用。为了解决这个问题，我们提出了一种流行的三胞胎损失的新型组合，以通过微调现有面部识别模型来提高对图像分辨率的鲁棒性。随着八度损失，我们利用高分辨率图像及其合成下采样的变体与其身份标签之间的关系。通过我们的方法对几种最先进的方法进行微调证明，我们可以显着提高各种数据集的跨分辨率（高低分辨率）面部验证的性能，而不会有意义地加剧高分辨率图像的性能。我们对FaceTransFormer网络应用的方法在挑战性的XQLFW数据集上达到95.12％的面对验证精度，同时在LFW数据库上达到99.73％。此外，低到低面验证精度从我们的方法中受益。我们发布我们的代码，以允许将Octuleplet损失的无缝集成到现有框架中。
Image resolution, or in general, image quality, plays an essential role in the performance of today's face recognition systems. To address this problem, we propose a novel combination of the popular triplet loss to improve robustness against image resolution via fine-tuning of existing face recognition models. With octuplet loss, we leverage the relationship between high-resolution images and their synthetically down-sampled variants jointly with their identity labels. Fine-tuning several state-of-the-art approaches with our method proves that we can significantly boost performance for cross-resolution (high-to-low resolution) face verification on various datasets without meaningfully exacerbating the performance on high-to-high resolution images. Our method applied on the FaceTransformer network achieves 95.12% face verification accuracy on the challenging XQLFW dataset while reaching 99.73% on the LFW database. Moreover, the low-to-low face verification accuracy benefits from our method. We release our code to allow seamless integration of the octuplet loss into existing frameworks.
