A framework for learning an accurate and general parametric facial model from 2D images is proposed and its application for analyzing and synthesizing facial images with pose variation is demonstrated. Our parametric piecewise linear subspace method covers a wide range of pose variation in a continuous manner through a weighted linear combination of local linear models distributed inapose parameter space. The linear design helps to avoid typical non-linear pitfalls such as overfitting and time-consuming learning. Experimental results show sub-degree and sub-pixel accuracy within \pm55 degree full 3D rotation and good generalization capability over unknown head poses when learned and tested for specific persons.