  Face detection is a well-explored problem. Many challenges on face detectors
like extreme pose, illumination, low resolution and small scales are studied in
the previous work. However, previous proposed models are mostly trained and
tested on good-quality images which are not always the case for practical
applications like surveillance systems. In this paper, we first review the
current state-of-the-art face detectors and their performance on benchmark
dataset FDDB, and compare the design protocols of the algorithms. Secondly, we
investigate their performance degradation while testing on low-quality images
with different levels of blur, noise, and contrast. Our results demonstrate
that both hand-crafted and deep-learning based face detectors are not robust
enough for low-quality images. It inspires researchers to produce more robust
design for face detection in the wild.
