# Unknown Identity Rejection Loss: Utilizing Unlabeled Data for Face Recognition

arXiv: https://arxiv.org/abs/1910.10896 · HTML: https://mlsanity.com/paper/1910.10896/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [Open-set Face Recognition using Ensembles trained on Clustered Data](https://mlsanity.com/paper/2308.07445/) (2023)
- [A Survey of Face Recognition](https://mlsanity.com/paper/2212.13038/) (2022)
- [Database-Agnostic Gait Enrollment using SetTransformers](https://mlsanity.com/paper/2505.02815/) (2025)

## Abstract (arXiv)

Face recognition has advanced considerably with the availability of large-scale labeled datasets. However, how to further improve the performance with the easily accessible unlabeled dataset remains a challenge. In this paper, we propose the novel Unknown Identity Rejection (UIR) loss to utilize the unlabeled data. We categorize identities in unconstrained environment into the known set and the unknown set. The former corresponds to the identities that appear in the labeled training dataset while the latter is its complementary set. Besides training the model to accurately classify the known identities, we also force the model to reject unknown identities provided by the unlabeled dataset via our proposed UIR loss. In order to 'reject' faces of unknown identities, centers of the known identities are forced to keep enough margin from centers of unknown identities which are assumed to be approximated by the features of their samples. By this means, the discriminativeness of the face representations can be enhanced. Experimental results demonstrate that our approach can provide obvious performance improvement by utilizing the unlabeled data.