Title: Face Detection using Deep Learning: An Improved Faster RCNN Approach,
Abstract: In this report, we present a new face detection scheme using deep learning
and achieve the state-of-the-art detection performance on the well-known FDDB
face detetion benchmark evaluation. In particular, we improve the
state-of-the-art faster RCNN framework by combining a number of strategies,
including feature concatenation, hard negative mining, multi-scale training,
model pretraining, and proper calibration of key parameters. As a consequence,
the proposed scheme obtained the state-of-the-art face detection performance,
making it the best model in terms of ROC curves among all the published methods
on the FDDB benchmark.