UniTSFace: Unified Threshold Integrated Sample-to-Sample Loss for Face
  Recognition
Qiufu Li Xi Jia Jiancan Zhou Linlin Shen Jinming Duan
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.