Inverted Non-maximum Suppression for more Accurate and Neater Face
  Detection
Lian Liu liguo Zhou
CNN-based face detection methods have achieved significant progress in recent
years. In addition to the strong representation ability of CNN, post-processing
methods are also very important for the performance of face detection. In
general, the face detection method predicts several candidate bounding-boxes
for one face. NMS is used to filter out inaccurate candidate boxes to get the
most accurate box. The principle of NMS is to select the box with a higher
score as the basic box and then delete the box which has a large overlapping
area with the basic box but has a lower score. However, the current NMS method
and its improved versions do not perform well when face image quality is poor
or faces are in a cluster. In these situations, even after NMS filtering, there
is often a face corresponding to multiple predicted boxes. To reduce this kind
of negative result, in this paper, we propose a new NMS method that operates in
the reverse order of other NMS methods. Our method performs well on low-quality
and tiny face samples. Experiments demonstrate that our method is effective as
a post-processor for different face detection methods.