  Robust face detection is one of the most important pre-processing steps to
support facial expression analysis, facial landmarking, face recognition, pose
estimation, building of 3D facial models, etc. Although this topic has been
intensely studied for decades, it is still challenging due to numerous variants
of face images in real-world scenarios. In this paper, we present a novel
approach named Multiple Scale Faster Region-based Convolutional Neural Network
(MS-FRCNN) to robustly detect human facial regions from images collected under
various challenging conditions, e.g. large occlusions, extremely low
resolutions, facial expressions, strong illumination variations, etc. The
proposed approach is benchmarked on two challenging face detection databases,
i.e. the Wider Face database and the Face Detection Dataset and Benchmark
(FDDB), and compared against recent other face detection methods, e.g.
Two-stage CNN, Multi-scale Cascade CNN, Faceness, Aggregate Chanel Features,
HeadHunter, Multi-view Face Detection, Cascade CNN, etc. The experimental
results show that our proposed approach consistently achieves highly
competitive results with the state-of-the-art performance against other recent
face detection methods.
