Title: Face R-CNN,
Abstract: Faster R-CNN is one of the most representative and successful methods for
object detection, and has been becoming increasingly popular in various
objection detection applications. In this report, we propose a robust deep face
detection approach based on Faster R-CNN. In our approach, we exploit several
new techniques including new multi-task loss function design, online hard
example mining, and multi-scale training strategy to improve Faster R-CNN in
multiple aspects. The proposed approach is well suited for face detection, so
we call it Face R-CNN. Extensive experiments are conducted on two most popular
and challenging face detection benchmarks, FDDB and WIDER FACE, to demonstrate
the superiority of the proposed approach over state-of-the-arts.