# On Low-Resolution Face Recognition in the Wild: Comparisons and New Techniques

arXiv: https://arxiv.org/abs/1805.11529 · HTML: https://mlsanity.com/paper/1805.11529/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2018.

## Built on by (most cited first, 30 of 34)

- [Student Network Learning via Evolutionary Knowledge Distillation](https://mlsanity.com/paper/2103.13811/) (2021)
- [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)
- [Introduction to Presentation Attack Detection in Face Biometrics and Recent Advances](https://mlsanity.com/paper/2111.11794/) (2021)
- [High-Similarity-Pass Attention for Single Image Super-Resolution](https://mlsanity.com/paper/2305.15768/) (2023)
- [Deep Rival Penalized Competitive Learning for Low-resolution Face Recognition](https://mlsanity.com/paper/2108.01286/) (2021)
- [Face Super-Resolution Using Stochastic Differential Equations](https://mlsanity.com/paper/2209.12064/) (2022)
- [PETALface: Parameter Efficient Transfer Learning for Low-resolution Face Recognition](https://mlsanity.com/paper/2412.07771/) (2024)
- [Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition](https://mlsanity.com/paper/2103.13851/) (2021)
- [Surveillance Face Presentation Attack Detection Challenge](https://mlsanity.com/paper/2304.07580/) (2023)
- [Optimization-Based Improvement of Face Image Quality Assessment Techniques](https://mlsanity.com/paper/2305.14856/) (2023)
- [Efficient Low-Resolution Face Recognition via Bridge Distillation](https://mlsanity.com/paper/2409.11786/) (2024)
- [Assessment Framework for Deepfake Detection in Real-world Situations](https://mlsanity.com/paper/2304.06125/) (2023)
- [Look One and More: Distilling Hybrid Order Relational Knowledge for Cross-Resolution Image Recognition](https://mlsanity.com/paper/2409.05384/) (2024)
- [A New Approach to Improve Learning-based Deepfake Detection in Realistic Conditions](https://mlsanity.com/paper/2203.11807/) (2022)
- [Beyond Spatial Explanations: Explainable Face Recognition in the Frequency Domain](https://mlsanity.com/paper/2407.11941/) (2024)
- [Low-Resolution Object Recognition with Cross-Resolution Relational Contrastive Distillation](https://mlsanity.com/paper/2409.02555/) (2024)
- [LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models](https://mlsanity.com/paper/2502.03950/) (2025)
- [Reconstruct Face from Features Using GAN Generator as a Distribution Constraint](https://mlsanity.com/paper/2206.04295/) (2022)
- [Dive into the Resolution Augmentations and Metrics in Low Resolution Face Recognition: A Plain yet Effective New Baseline](https://mlsanity.com/paper/2302.05621/) (2023)
- [Impact of Video Processing Operations in Deepfake Detection](https://mlsanity.com/paper/2303.17247/) (2023)
- [Automated Visual Attention Detection using Mobile Eye Tracking in Behavioral Classroom Studies](https://mlsanity.com/paper/2505.07552/) (2025)
- [Susceptibility to Image Resolution in Face Recognition and Trainings Strategies](https://mlsanity.com/paper/2107.03769/) (2021)
- [(Un)fair Exposure in Deep Face Rankings at a Distance](https://mlsanity.com/paper/2308.11732/) (2023)
- [Text-Guided Face Recognition using Multi-Granularity Cross-Modal Contrastive Learning](https://mlsanity.com/paper/2312.09367/) (2023)
- [ROFI: A Deep Learning-Based Ophthalmic Sign-Preserving and Reversible Patient Face Anonymizer](https://mlsanity.com/paper/2510.11073/) (2025)
- [Robust Onion: Peeling Open Vocab Object Detectors Under Noise](https://mlsanity.com/paper/2606.26734/) (2026)
- [Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face Recognition](https://mlsanity.com/paper/2101.10845/) (2021)
- [Profile to Frontal Face Recognition in the Wild Using Coupled Conditional GAN](https://mlsanity.com/paper/2107.13742/) (2021)
- [A Novel Framework for Assessment of Learning-based Detectors in Realistic Conditions with Application to Deepfake Detection](https://mlsanity.com/paper/2203.11797/) (2022)

## Abstract (arXiv)

Although face recognition systems have achieved impressive performance in recent years, the low-resolution face recognition (LRFR) task remains challenging, especially when the LR faces are captured under non-ideal conditions, as is common in surveillance-based applications. Faces captured in such conditions are often contaminated by blur, nonuniform lighting, and nonfrontal face pose. In this paper, we analyze face recognition techniques using data captured under low-quality conditions in the wild. We provide a comprehensive analysis of experimental results for two of the most important applications in real surveillance applications, and demonstrate practical approaches to handle both cases that show promising performance. The following three contributions are made: em (i) we conduct experiments to evaluate super-resolution methods for low-resolution face recognition; em (ii) we study face re-identification on various public face datasets including real surveillance and low-resolution subsets of large-scale datasets, present a baseline result for several deep learning based approaches, and improve them by introducing a GAN pre-training approach and fully convolutional architecture; and em (iii) we explore low-resolution face identification by employing a state-of-the-art supervised discriminative learning approach. Evaluations are conducted on challenging portions of the SCFace and UCCSface datasets.