UniTSFace: Unified Threshold Integrated Sample-to-Sample Loss for Face Recognition Qiufu Li1, 2,6, # Xi Jia 1,2, 3,# Jiancan Zhou

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.