# Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results

arXiv: https://arxiv.org/abs/1804.10275 · HTML: https://mlsanity.com/paper/1804.10275/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2018.

## Built on by (most cited first, 16 of 16)

- [Unsupervised Domain Adaptation of Object Detectors: A Survey](https://mlsanity.com/paper/2105.13502/) (2021)
- [Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection](https://mlsanity.com/paper/2205.03346/) (2022)
- [Surveillance Face Anti-spoofing](https://mlsanity.com/paper/2301.00975/) (2023)
- [Going Deeper Into Face Detection: A Survey](https://mlsanity.com/paper/2103.14983/) (2021)
- [Detect Faces Efficiently: A Survey and Evaluations](https://mlsanity.com/paper/2112.01787/) (2021)
- [Practical exposure correction via compensation](https://mlsanity.com/paper/2212.14245/) (2022)
- [Privacy-preserving Online AutoML for Domain-Specific Face Detection](https://mlsanity.com/paper/2203.08399/) (2022)
- [Benchmarking Shadow Removal for Facial Landmark Detection and Beyond](https://mlsanity.com/paper/2111.13790/) (2021)
- [Embodied Domain Adaptation for Object Detection](https://mlsanity.com/paper/2506.21860/) (2025)
- [EMface: Detecting Hard Faces by Exploring Receptive Field Pyraminds](https://mlsanity.com/paper/2105.10104/) (2021)
- [How far have we gone in Generative Image Restoration? A study on its capability, limitations and evaluation practices](https://mlsanity.com/paper/2603.05010/) (2026)
- [ODOV: Benchmark the Open-Domain Open-Vocabulary Object Detection](https://mlsanity.com/paper/2508.01253/) (2025)
- [1st Place Solutions for UG2+ Challenge 2021 -- (Semi-)supervised Face detection in the low light condition](https://mlsanity.com/paper/2107.00818/) (2021)
- [Fast Eye Detector Using Siamese Network for NIR Partial Face Images](https://mlsanity.com/paper/2202.10671/) (2022)
- [Peng Cheng Object Detection Benchmark for Smart City](https://mlsanity.com/paper/2203.05949/) (2022)
- [EfficientDepth: A Fast and Detail-Preserving Monocular Depth Estimation Model](https://mlsanity.com/paper/2509.22527/) (2025)

## Abstract (arXiv)

Face detection has witnessed immense progress in the last few years, with new milestones being surpassed every year. While many challenges such as large variations in scale, pose, appearance are successfully addressed, there still exist several issues which are not specifically captured by existing methods or datasets. In this work, we identify the next set of challenges that requires attention from the research community and collect a new dataset of face images that involve these issues such as weather-based degradations, motion blur, focus blur and several others. We demonstrate that there is a considerable gap in the performance of state-of-the-art detectors and real-world requirements. Hence, in an attempt to fuel further research in unconstrained face detection, we present a new annotated Unconstrained Face Detection Dataset (UFDD) with several challenges and benchmark recent methods. Additionally, we provide an in-depth analysis of the results and failure cases of these methods. The dataset as well as baseline results will be made publicly available in due time. The UFDD dataset as well as baseline results are available at: www.ufdd.info/