Sample-to-class-based face recognition models can not fully explore the cross-sample relationship among large amounts of facial images, while sample-to-sample-based models require sophisticated pairing processes for training. Furthermore, neither method satisfies the requirements of real-world face verification applications, which expect a unified threshold separating positive from negative facial pairs. In this paper, we propose a unified threshold integrated sample-to-sample based loss (USS loss), which features an explicit unified threshold for distinguishing positive from negative pairs. Inspired by our USS loss, we also derive the sample-to-sample based softmax and BCE losses, and discuss their relationship. Extensive evaluation on multiple benchmark datasets, including MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace, demonstrates that the proposed USS loss is highly efficient and can work seamlessly with sample-to-class-based losses. The embedded loss (USS and sample-to-class Softmax loss) overcomes the pitfalls of previous approaches and the trained facial model UniTSFace exhibits exceptional performance, outperforming state-of-the-art methods, such as CosFace, ArcFace, VPL, AnchorFace, and UNPG. Our code is available.

翻译：基于样本-类的人脸识别模型无法充分挖掘海量人脸图像中的样本间关联，而基于样本-对的模型则需要复杂的配对流程进行训练。此外，这两种方法均无法满足真实世界人脸验证应用的需求——这类应用期望存在一个统一阈值来区分正负人脸对。本文提出一种统一阈值集成的样本间损失函数（USS损失），其核心特色在于采用显式统一阈值区分正负样本对。受USS损失启发，我们还推导了基于样本对的softmax损失与BCE损失，并探讨了它们之间的关联性。在MFR、IJB-C、LFW、CFP-FP、AgeDB及MegaFace等多个基准数据集上的广泛评估表明，所提出的USS损失具有高效性，且能与基于样本-类的损失无缝协同工作。嵌入损失（USS与样本-类Softmax损失的结合）克服了先前方法的缺陷，训练得到的人脸模型UniTSFace展现出卓越性能，超越了CosFace、ArcFace、VPL、AnchorFace及UNPG等当前最优方法。我们的代码已公开。