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This paper presents a component based deformable model for generalized face alignment, in which a novel bi-stage statistical framework is proposed to account for both local and global shape characteristics. Instead of using statistical analysis on the entire shape as in previous alignment work, we build separate Gaussian models for shape components to preserve more detailed local shape deformations. In each model of components the Markov Network is integrated to provide simple geometry constraints for our search strategy. In order to make a better description of the nonlinear interrelationships over the shape components, the Gaussian process latent variable model is adopted to obtain enough control of full range shape variations. Furthermore, we propose an illumination-robust feature to lead the local fitting of every shape point when light conditions change dramatically. Based on this approach, our system can generate optimal shape for images with exaggerated expressions and under variable illumination, as evidenced by extensive experimentation.