# Toward Open-Set Face Recognition

arXiv: https://arxiv.org/abs/1705.01567 · HTML: https://mlsanity.com/paper/1705.01567/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2017.

## Built on by (most cited first, 29 of 29)

- [SSD: A Unified Framework for Self-Supervised Outlier Detection](https://mlsanity.com/paper/2103.12051/) (2021)
- [A Survey on Open Set Recognition](https://mlsanity.com/paper/2109.00893/) (2021)
- [Person Recognition at Altitude and Range: Fusion of Face, Body Shape and Gait](https://mlsanity.com/paper/2505.04616/) (2025)
- [Open-Set Biometrics: Beyond Good Closed-Set Models](https://mlsanity.com/paper/2407.16133/) (2024)
- [Universal Adversarial Spoofing Attacks against Face Recognition](https://mlsanity.com/paper/2110.00708/) (2021)
- [Open-Set Face Recognition with Maximal Entropy and Objectosphere Loss](https://mlsanity.com/paper/2311.00400/) (2023)
- [LocalScore: Local Density-Aware Similarity Scoring for Biometrics](https://mlsanity.com/paper/2602.01012/) (2026)
- [ScaleFace: Uncertainty-aware Deep Metric Learning](https://mlsanity.com/paper/2209.01880/) (2022)
- [Open-set Face Recognition using Ensembles trained on Clustered Data](https://mlsanity.com/paper/2308.07445/) (2023)
- [All Beings Are Equal in Open Set Recognition](https://mlsanity.com/paper/2401.17654/) (2024)
- [Holistic Uncertainty Estimation For Open-Set Recognition](https://mlsanity.com/paper/2408.14229/) (2024)
- [Open-set Face Recognition for Small Galleries Using Siamese Networks](https://mlsanity.com/paper/2105.06967/) (2021)
- [LORD: Leveraging Open-Set Recognition with Unknown Data](https://mlsanity.com/paper/2308.12584/) (2023)
- [A Metacognitive Approach to Out-of-Distribution Detection for Segmentation](https://mlsanity.com/paper/2311.07578/) (2023)
- [CamPro: Camera-based Anti-Facial Recognition](https://mlsanity.com/paper/2401.00151/) (2023)
- [Large-Scale Evaluation of Open-Set Image Classification Techniques](https://mlsanity.com/paper/2406.09112/) (2024)
- [Are you In or Out (of gallery)? Wisdom from the Same-Identity Crowd](https://mlsanity.com/paper/2508.06357/) (2025)
- [Exploring the Open World Using Incremental Extreme Value Machines](https://mlsanity.com/paper/2205.14892/) (2022)
- [Open-Set Face Identification on Few-Shot Gallery by Fine-Tuning](https://mlsanity.com/paper/2301.01922/) (2023)
- [Open-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature Augmentation](https://mlsanity.com/paper/2308.12371/) (2023)
- [FaceGCD: Generalized Face Discovery via Dynamic Prefix Generation](https://mlsanity.com/paper/2507.22353/) (2025)
- [Database-Agnostic Gait Enrollment using SetTransformers](https://mlsanity.com/paper/2505.02815/) (2025)
- [EVINET: Towards Open-World Graph Learning via Evidential Reasoning Network](https://mlsanity.com/paper/2506.07288/) (2025)
- [Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition](https://mlsanity.com/paper/2509.06570/) (2025)
- [A Universal and Robust Framework for Multiple Gas Recognition Based-on Spherical Normalization-Coupled Mahalanobis Algorithm](https://mlsanity.com/paper/2512.22792/) (2025)
- [Uncertainty Estimation for the Open-Set Text Classification systems](https://mlsanity.com/paper/2604.08560/) (2026)
- [Non-frontal face recognition using GANs and memristor-based classifiers](https://mlsanity.com/paper/2606.12074/) (2026)
- [Unlearnable Faces: Privacy Protection Surviving Extraction Pipeline](https://mlsanity.com/paper/2607.05996/) (2026)
- [Rank-1 Identity Consensus Predicts Gallery Enrollment in 1:N Face Matching More Accurately than Score Thresholding](https://mlsanity.com/paper/2607.12903/) (2026)

## Abstract (arXiv)

Much research has been conducted on both face identification and face verification, with greater focus on the latter. Research on face identification has mostly focused on using closed-set protocols, which assume that all probe images used in evaluation contain identities of subjects that are enrolled in the gallery. Real systems, however, where only a fraction of probe sample identities are enrolled in the gallery, cannot make this closed-set assumption. Instead, they must assume an open set of probe samples and be able to reject/ignore those that correspond to unknown identities. In this paper, we address the widespread misconception that thresholding verification-like scores is a good way to solve the open-set face identification problem, by formulating an open-set face identification protocol and evaluating different strategies for assessing similarity. Our open-set identification protocol is based on the canonical labeled faces in the wild (LFW) dataset. Additionally to the known identities, we introduce the concepts of known unknowns (known, but uninteresting persons) and unknown unknowns (people never seen before) to the biometric community. We compare three algorithms for assessing similarity in a deep feature space under an open-set protocol: thresholded verification-like scores, linear discriminant analysis (LDA) scores, and an extreme value machine (EVM) probabilities. Our findings suggest that thresholding EVM probabilities, which are open-set by design, outperforms thresholding verification-like scores.