Fast cascade face detection with pyramid network

Abstract

Numerous recent face detectors based on convolutional neural networks (CNNs) have significantly improved the detection performance. However, CNNs usually have a huge number of parameters which lead to very low detection speeds. To address this issue, this paper proposes a fast CNNs cascade face detector with multi-task learning and network acceleration techniques. In particular, the first stage of the detector is an elaborately designed fully convolutional network with a novel pyramid architecture, which can generate multi-scale face proposals efficiently with no more than twice image resizing operations. Several network compression and acceleration techniques including multi-layer merging and knowledge distilling are adopted to further improve inference speed. In addition, online and offline hard sample mining are jointly utilized to further strengthen the power of networks. The experimental results on challenging FDDB show that the proposed face detector is comparable in performance with the-state-of-arts while the speed reaches astonishing 165 fps on Titan GPU.