  Face detection is a widely studied problem over the past few decades.
Recently, significant improvements have been achieved via the deep neural
network, however, it is still challenging to directly apply these techniques to
mobile devices for its limited computational power and memory. In this work, we
present a proposal generation acceleration framework for real-time face
detection. More specifically, we adopt a popular cascaded convolutional neural
network (CNN) as the basis, then apply our acceleration approach on the basic
framework to speed up the model inference time. We are motivated by the
observation that the computation bottleneck of this framework arises from the
proposal generation stage, where each level of the dense image pyramid has to
go through the network. In this work, we reduce the number of image pyramid
levels by utilizing both global and local facial characteristics (i.e., global
face and facial parts). Experimental results on public benchmarks WIDER-face
and FDDB demonstrate the satisfactory performance and faster speed compared to
the state-of-the-arts. %the comparable accuracy to state-of-the-arts with
faster speed.
