  Recent research on face detection, which is focused primarily on improving
accuracy of detecting smaller faces, attempt to develop new anchor design
strategies to facilitate increased overlap between anchor boxes and ground
truth faces of smaller sizes. In this work, we approach the problem of small
face detection with the motivation of enriching the feature maps using a
density map estimation module. This module, inspired by recent crowd
counting/density estimation techniques, performs the task of estimating the per
pixel density of people/faces present in the image. Output of this module is
employed to accentuate the feature maps from the backbone network using a
feature enrichment module before being used for detecting smaller faces. The
proposed approach can be used to complement recent anchor-design based novel
methods to further improve their results. Experiments conducted on different
datasets such as WIDER, FDDB and Pascal-Faces demonstrate the effectiveness of
the proposed approach.
