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 degra- dations, 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 require- ments. 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.



Evaluation results

                                            ă€€Evaluation results of different algorithms, that are pre-trained on WIDER FACE, on the proposed UFDD dataset.

[Reproducible Code -Google Drive-]
  [Reproducible Code -Baidu Yun-]


  title={Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results},
  author={Nada, Hajime and Sindagi, Vishwanath and Zhang, He and Patel, Vishal M},
  journal={arXiv preprint arXiv:1804.10275},


 Authors: Hajime Nada, Vishwanath Sindagi, He Zhang, Vishal M. Patel.
 Email: team.ufdd(AT)gmail.com