Boosting Pseudo Census Transform Features for Face Alignment

Face alignment using deformable face model has attracted broad interest in recent years for its wide range of applications in facial analysis. Previous work has shown that discriminative deformable models have better generalization capacity compared to generative models [8, 9]. In this paper, we present a new discriminative face model based on boosting pseudo census transform features. This feature is considered to be less sensitive to illumination changes, which yields a more robust alignment algorithm. The alignment is based on maximizing the scores of boosted strong classifier, which indicate whether the current alignment is a correct or incorrect one. The proposed approach has been evaluated extensively on several databases. The experimental results show that our approach generalizes better on unseen data compared to the Haar feature-based approach. Moreover, its training procedure is much faster due to the low dimensionality of the configuration space of the proposed feature.