# Open-set Face Recognition using Ensembles trained on Clustered Data

arXiv: https://arxiv.org/abs/2308.07445 · HTML: https://mlsanity.com/paper/2308.07445/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 14 Aug 2023.

## Builds on (most cited first, 11 of 11)

- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [Additive Margin Softmax for Face Verification](https://mlsanity.com/paper/1801.05599/) (2018)
- [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](https://mlsanity.com/paper/1512.00596/) (2015)
- [The Extreme Value Machine](https://mlsanity.com/paper/1506.06112/) (2015)
- [DeepID3: Face Recognition with Very Deep Neural Networks](https://mlsanity.com/paper/1502.00873/) (2015)
- [Toward Open-Set Face Recognition](https://mlsanity.com/paper/1705.01567/) (2017)
- [Unconstrained Face Detection and Open-Set Face Recognition Challenge](https://mlsanity.com/paper/1708.02337/) (2017)
- [Template Adaptation for Face Verification and Identification](https://mlsanity.com/paper/1603.03958/) (2016)
- [Shared Representation Learning for Heterogeneous Face Recognition](https://mlsanity.com/paper/1406.1247/) (2014)
- [Unknown Identity Rejection Loss: Utilizing Unlabeled Data for Face Recognition](https://mlsanity.com/paper/1910.10896/) (2019)

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

- [Open-Set Face Recognition with Maximal Entropy and Objectosphere Loss](https://mlsanity.com/paper/2311.00400/) (2023)
- [Open-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature Augmentation](https://mlsanity.com/paper/2308.12371/) (2023)
- [Non-frontal face recognition using GANs and memristor-based classifiers](https://mlsanity.com/paper/2606.12074/) (2026)

## Abstract (arXiv)

Open-set face recognition describes a scenario where unknown subjects, unseen during the training stage, appear on test time. Not only it requires methods that accurately identify individuals of interest, but also demands approaches that effectively deal with unfamiliar faces. This work details a scalable open-set face identification approach to galleries composed of hundreds and thousands of subjects. It is composed of clustering and an ensemble of binary learning algorithms that estimates when query face samples belong to the face gallery and then retrieves their correct identity. The approach selects the most suitable gallery subjects and uses the ensemble to improve prediction performance. We carry out experiments on well-known LFW and YTF benchmarks. Results show that competitive performance can be achieved even when targeting scalability.