We propose a novel end-to-end deep architecture for face landmark detection,
based on a deep convolutional and deconvolutional network followed by carefully
designed recurrent network structures. The pipeline of this architecture
consists of three parts. Through the first part, we encode an input face image
to resolution-preserved deconvolutional feature maps via a deep network with
stacked convolutional and deconvolutional layers. Then, in the second part, we
estimate the initial coordinates of the facial key points by an additional
convolutional layer on top of these deconvolutional feature maps. In the last
part, by using the deconvolutional feature maps and the initial facial key
points as input, we refine the coordinates of the facial key points by a
recurrent network that consists of multiple Long-Short Term Memory (LSTM)
components. Extensive evaluations on several benchmark datasets show that the
proposed deep architecture has superior performance against the
state-of-the-art methods.

提出一种新颖的端到端深度架构用于面部标记检测，它基于卷积和反卷积网络，以及经过精心设计的循环网络结构，并在多个基准数据集上得到了比当前最先进方法更卓越的表现。