  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/
