  We aim to study the multi-scale receptive fields of a single convolutional
neural network to detect faces of varied scales. This paper presents our
Multi-Scale Receptive Field Face Detector (MSFD), which has superior
performance on detecting faces at different scales and enjoys real-time
inference speed. MSFD agglomerates context and texture by hierarchical
structure. More additional information and rich receptive field bring
significant improvement but generate marginal time consumption. We
simultaneously propose an anchor assignment strategy which can cover faces with
a wide range of scales to improve the recall rate of small faces and rotated
faces. To reduce the false positive rate, we train our detector with focal loss
which keeps the easy samples from overwhelming. As a result, MSFD reaches
superior results on the FDDB, Pascal-Faces and WIDER FACE datasets, and can run
at 31 FPS on GPU for VGA-resolution images.
