  Face detection and alignment in unconstrained environment are challenging due
to various poses, illuminations and occlusions. Recent studies show that deep
learning approaches can achieve impressive performance on these two tasks. In
this paper, we propose a deep cascaded multi-task framework which exploits the
inherent correlation between them to boost up their performance. In particular,
our framework adopts a cascaded structure with three stages of carefully
designed deep convolutional networks that predict face and landmark location in
a coarse-to-fine manner. In addition, in the learning process, we propose a new
online hard sample mining strategy that can improve the performance
automatically without manual sample selection. Our method achieves superior
accuracy over the state-of-the-art techniques on the challenging FDDB and WIDER
FACE benchmark for face detection, and AFLW benchmark for face alignment, while
keeps real time performance.
