  Convolutional neural network (CNN) based face detectors are inefficient in
handling faces of diverse scales. They rely on either fitting a large single
model to faces across a large scale range or multi-scale testing. Both are
computationally expensive. We propose Scale-aware Face Detector (SAFD) to
handle scale explicitly using CNN, and achieve better performance with less
computation cost. Prior to detection, an efficient CNN predicts the scale
distribution histogram of the faces. Then the scale histogram guides the
zoom-in and zoom-out of the image. Since the faces will be approximately in
uniform scale after zoom, they can be detected accurately even with much
smaller CNN. Actually, more than 99% of the faces in AFW can be covered with
less than two zooms per image. Extensive experiments on FDDB, MALF and AFW show
advantages of SAFD.
