Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results
This work addresses the need for better face detection in real-world, unconstrained environments for computer vision researchers, though it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of unconstrained face detection by identifying gaps in existing methods and datasets, and they introduced a new dataset (UFDD) with challenges like weather degradations and blur, showing a considerable performance gap between state-of-the-art detectors and real-world requirements.
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/