  This paper presents a iterative optimization method, explicit shape
regression, for face pose detection and localization. The regression function
is learnt to find out the entire facial shape and minimize the alignment
errors. A cascaded learning framework is employed to enhance shape constraint
during detection. A combination of a two-level boosted regression, shape
indexed features and a correlation-based feature selection method is used to
improve the performance. In this paper, we have explain the advantage of ESR
for deformable object like face pose estimation and reveal its generic
applications of the method. In the experiment, we compare the results with
different work and demonstrate the accuracy and robustness in different
scenarios.
