# Recurrent Exposure Generation for Low-Light Face Detection

arXiv: https://arxiv.org/abs/2007.10963 · HTML: https://mlsanity.com/paper/2007.10963/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2020.

## Built on by (most cited first, 27 of 27)

- [Low-Light Image Enhancement with Wavelet-based Diffusion Models](https://mlsanity.com/paper/2306.00306/) (2023)
- [HLA-Face: Joint High-Low Adaptation for Low Light Face Detection](https://mlsanity.com/paper/2104.01984/) (2021)
- [Human Pose Estimation in Extremely Low-Light Conditions](https://mlsanity.com/paper/2303.15410/) (2023)
- [Tracker Meets Night: A Transformer Enhancer for UAV Tracking](https://mlsanity.com/paper/2303.10951/) (2023)
- [Learning with Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision](https://mlsanity.com/paper/2112.04719/) (2021)
- [GenISP: Neural ISP for Low-Light Machine Cognition](https://mlsanity.com/paper/2205.03688/) (2022)
- [Bilevel Fast Scene Adaptation for Low-Light Image Enhancement](https://mlsanity.com/paper/2306.01343/) (2023)
- [FeatEnHancer: Enhancing Hierarchical Features for Object Detection and Beyond Under Low-Light Vision](https://mlsanity.com/paper/2308.03594/) (2023)
- [NOD: Taking a Closer Look at Detection under Extreme Low-Light Conditions with Night Object Detection Dataset](https://mlsanity.com/paper/2110.10364/) (2021)
- [Noise Self-Regression: A New Learning Paradigm to Enhance Low-Light Images Without Task-Related Data](https://mlsanity.com/paper/2211.04700/) (2022)
- [Searching a Compact Architecture for Robust Multi-Exposure Image Fusion](https://mlsanity.com/paper/2305.12236/) (2023)
- [Fearless Luminance Adaptation: A Macro-Micro-Hierarchical Transformer for Exposure Correction](https://mlsanity.com/paper/2309.00872/) (2023)
- [Unsupervised Image Prior via Prompt Learning and CLIP Semantic Guidance for Low-Light Image Enhancement](https://mlsanity.com/paper/2405.11478/) (2024)
- [Practical exposure correction via compensation](https://mlsanity.com/paper/2212.14245/) (2022)
- [Holistic Dynamic Frequency Transformer for Image Fusion and Exposure Correction](https://mlsanity.com/paper/2309.01183/) (2023)
- [Zero-Reference Lighting Estimation Diffusion Model for Low-Light Image Enhancement](https://mlsanity.com/paper/2403.02879/) (2024)
- [Trash to Treasure: Low-Light Object Detection via Decomposition-and-Aggregation](https://mlsanity.com/paper/2309.03548/) (2023)
- [Leveraging Content and Context Cues for Low-Light Image Enhancement](https://mlsanity.com/paper/2412.07693/) (2024)
- [Striving for Faster and Better: A One-Layer Architecture with Auto Re-parameterization for Low-Light Image Enhancement](https://mlsanity.com/paper/2502.19867/) (2025)
- [Bilevel Generative Learning for Low-Light Vision](https://mlsanity.com/paper/2308.03381/) (2023)
- [Deep Decomposition and Bilinear Pooling Network for Blind Night-Time Image Quality Evaluation](https://mlsanity.com/paper/2205.05880/) (2022)
- [Rethinking Model Redundancy for Low-light Image Enhancement](https://mlsanity.com/paper/2412.16459/) (2024)
- [Recognition-Oriented Low-Light Image Enhancement based on Global and Pixelwise Optimization](https://mlsanity.com/paper/2501.04210/) (2025)
- [PDE: Gene Effect Inspired Parameter Dynamic Evolution for Low-light Image Enhancement](https://mlsanity.com/paper/2505.09196/) (2025)
- [Evaluation of facial landmark localization performance in a surgical setting](https://mlsanity.com/paper/2507.18248/) (2025)
- [AMIEOD: Adaptive Multi-Experts Image Enhancement for Object Detection in Low-Illumination Scenes](https://mlsanity.com/paper/2605.06084/) (2026)
- [Overlapped Wavelet Diffusion for Low-Light Image Enhancement](https://mlsanity.com/paper/2606.10280/) (2026)

## Abstract (arXiv)

Face detection from low-light images is challenging due to limited photos and inevitable noise, which, to make the task even harder, are often spatially unevenly distributed. A natural solution is to borrow the idea from multi-exposure, which captures multiple shots to obtain well-exposed images under challenging conditions. High-quality implementation/approximation of multi-exposure from a single image is however nontrivial. Fortunately, as shown in this paper, neither is such high-quality necessary since our task is face detection rather than image enhancement. Specifically, we propose a novel Recurrent Exposure Generation (REG) module and couple it seamlessly with a Multi-Exposure Detection (MED) module, and thus significantly improve face detection performance by effectively inhibiting non-uniform illumination and noise issues. REG produces progressively and efficiently intermediate images corresponding to various exposure settings, and such pseudo-exposures are then fused by MED to detect faces across different lighting conditions. The proposed method, named REGDet, is the first `detection-with-enhancement' framework for low-light face detection. It not only encourages rich interaction and feature fusion across different illumination levels, but also enables effective end-to-end learning of the REG component to be better tailored for face detection. Moreover, as clearly shown in our experiments, REG can be flexibly coupled with different face detectors without extra low/normal-light image pairs for training. We tested REGDet on the DARK FACE low-light face benchmark with thorough ablation study, where REGDet outperforms previous state-of-the-arts by a significant margin, with only negligible extra parameters.