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Large-scale variations still pose a challenge in unconstrained facedetection. To the best of our knowledge, no current face detection algorithmcan detect a face as large as 800 x 800 pixels while simultaneously detectinganother one as small as 8 x 8 pixels within a single image with equally highaccuracy. We propose a two-stage cascaded face detection framework, Multi-PathRegion-based Convolutional Neural Network (MP-RCNN), that seamlessly combines adeep neural network with a classic learning strategy, to tackle this challenge.The first stage is a Multi-Path Region Proposal Network (MP-RPN) that proposesfaces at three different scales. It simultaneously utilizes three paralleloutputs of the convolutional feature maps to predict multi-scale candidate faceregions. The "atrous" convolution trick (convolution with up-sampled filters)and a newly proposed sampling layer for "hard" examples are embedded in MP-RPNto further boost its performance. The second stage is a Boosted Forestsclassifier, which utilizes deep facial features pooled from inside thecandidate face regions as well as deep contextual features pooled from a largerregion surrounding the candidate face regions. This step is included to furtherremove hard negative samples. Experiments show that this approach achievesstate-of-the-art face detection performance on the WIDER FACE dataset "hard"partition, outperforming the former best result by 9.6% for the AveragePrecision.

Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained "Hard Faces"