# Surveillance Face Recognition Challenge

arXiv: https://arxiv.org/abs/1804.09691 · HTML: https://mlsanity.com/paper/1804.09691/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2018.

## Built on by (most cited first, 18 of 18)

- [Recognizability Embedding Enhancement for Very Low-Resolution Face Recognition and Quality Estimation](https://mlsanity.com/paper/2304.10066/) (2023)
- [Surveillance Face Anti-spoofing](https://mlsanity.com/paper/2301.00975/) (2023)
- [Multi-Agent Semi-Siamese Training for Long-tail and Shallow Face Learning](https://mlsanity.com/paper/2105.04113/) (2021)
- [Evaluation of Human and Machine Face Detection using a Novel Distinctive Human Appearance Dataset](https://mlsanity.com/paper/2111.00660/) (2021)
- [Homogeneous Low-Resolution Face Recognition Method based Correlation Features](https://mlsanity.com/paper/2111.13175/) (2021)
- [(Un)fair Exposure in Deep Face Rankings at a Distance](https://mlsanity.com/paper/2308.11732/) (2023)
- [Efficient Verification-Based Face Identification](https://mlsanity.com/paper/2312.13240/) (2023)
- [Adversarial Watermarking for Face Recognition](https://mlsanity.com/paper/2409.16056/) (2024)
- [FANVID: A Benchmark for Face and License Plate Recognition in Low-Resolution Videos](https://mlsanity.com/paper/2506.07304/) (2025)
- [Scale Attention for Learning Deep Face Representation: A Study Against Visual Scale Variation](https://mlsanity.com/paper/2209.08788/) (2022)
- [A Comparative Analysis of the Face Recognition Methods in Video Surveillance Scenarios](https://mlsanity.com/paper/2211.02952/) (2022)
- [Learning Unified Representations for Multi-Resolution Face Recognition](https://mlsanity.com/paper/2310.09563/) (2023)
- [FunFace: Feature Utility and Norm Estimation for Face Recognition](https://mlsanity.com/paper/2604.26598/) (2026)
- [Non-frontal face recognition using GANs and memristor-based classifiers](https://mlsanity.com/paper/2606.12074/) (2026)
- [GaitFace: A Multimodal Dataset for Long-Range Person Identification](https://mlsanity.com/paper/2607.23542/) (2026)
- [Low-Quality Face Recognition using Center Aligned Representations and Local Margin Constraints](https://mlsanity.com/paper/2609.01014/) (2026)
- [Lightweight Pedestrian Head-Orientation Recognition Network for Safe Pedestrian-Vehicle Interaction](https://mlsanity.com/paper/2609.24193/) (2026)
- [A Deployment Study of Identity-Gated Drone Gesture Control](https://mlsanity.com/paper/2609.25511/) (2026)

## Abstract (arXiv)

Face recognition (FR) is one of the most extensively investigated problems in computer vision. Significant progress in FR has been made due to the recent introduction of the larger scale FR challenges, particularly with constrained social media web images, e.g. high-resolution photos of celebrity faces taken by professional photo-journalists. However, the more challenging FR in unconstrained and low-resolution surveillance images remains largely under-studied. To facilitate more studies on developing FR models that are effective and robust for low-resolution surveillance facial images, we introduce a new Surveillance Face Recognition Challenge, which we call the QMUL-SurvFace benchmark. This new benchmark is the largest and more importantly the only true surveillance FR benchmark to our best knowledge, where low-resolution images are not synthesised by artificial down-sampling of native high-resolution images. This challenge contains 463,507 face images of 15,573 distinct identities captured in real-world uncooperative surveillance scenes over wide space and time. As a consequence, it presents an extremely challenging FR benchmark. We benchmark the FR performance on this challenge using five representative deep learning face recognition models, in comparison to existing benchmarks. We show that the current state of the arts are still far from being satisfactory to tackle the under-investigated surveillance FR problem in practical forensic scenarios. Face recognition is generally more difficult in an open-set setting which is typical for surveillance scenarios, owing to a large number of non-target people (distractors) appearing open spaced scenes. This is evidently so that on the new Surveillance FR Challenge, the top-performing CentreFace deep learning FR model on the MegaFace benchmark can now only achieve 13.2% success rate (at Rank-20) at a 10% false alarm rate.