  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.
