Face detection is the basic step of many face analysis tasks. In practice, face detectors
usually run on mobile devices with limited memory and computing resources. Therefore, it is
important to keep face detectors lightweight. To this end, current methods usually focus on
directly designing lightweight detectors. Nevertheless, it is not fully explored whether the
resource consumption of these lightweight detectors can be further suppressed without too
much sacrifice on accuracy. In this study, we propose to apply the network pruning method …

Face detection is the basic step of many face-analysis tasks. In practice, face detectors
usually run on mobile devices with limited memory and computing resources. Therefore, it is
important to keep the face detectors lightweight. To this end, current methods usually focus
on directly design lightweight detectors. Nevertheless, the resource consumption of the
lightweight detectors could be further suppressed. In this paper, we propose to apply the
network pruning method to the lightweight face detection network, which can further reduce …