  Real-world face detection and alignment demand an advanced discriminative
model to address challenges by pose, lighting and expression. Illuminated by
the deep learning algorithm, some convolutional neural networks based face
detection and alignment methods have been proposed. Recent studies have
utilized the relation between face detection and alignment to make models
computationally efficiency, however they ignore the connection between each
cascade CNNs. In this paper, we propose an structure to propose higher quality
training data for End-to-End cascade network training, which give computers
more space to automatic adjust weight parameter and accelerate convergence.
Experiments demonstrate considerable improvement over existing detection and
alignment models.
