Improved Face Detection and Alignment using Cascade Deep Convolutional Network

Real-world face detection and alignment demand an advanced discriminative model to address challenges by pose, lighting and expression. Recent studies have utilized the relation between face detection and alignment to make models computationally efficiency, but they ignore the connection between each cascade CNNs. In this paper, we combine detection, calibration and alignment in each Cascade CNN and propose an HEM method for End-to-End cascade network training, which give computers more space to automatic adjust weight parameter and accelerate convergence. Experiments on FDDB and AFLW demonstrate considerable improvement on accuracy and speed.