  In this paper, we present a deep regression approach for face alignment. The
deep architecture consists of a global layer and multi-stage local layers. We
apply the back-propagation algorithm with the dropout strategy to jointly
optimize the regression parameters. We show that the resulting deep regressor
gradually and evenly approaches the true facial landmarks stage by stage,
avoiding the tendency to yield over-strong early stage regressors while
over-weak later stage regressors. Experimental results show that our approach
achieves the state-of-the-art
