Weighted regularized ASM for face alignment

Active Shape Models are a powerful and well known method to perform face alignment. In some applications it is common to have shape information available beforehand, such as previously detected landmarks. Introducing this prior knowledge to the statistical model may result of great advantage but it is challenging to maintain this priors unchanged once the statistical model constraints are applied. We propose a new weighted-regularized projection into the parameter space which allows us to obtain shapes that at the same time fulfill the imposed shape constraints and are plausible according to the statistical model. The performed experiments show how using this projection better performance than competing state of the art methods is achieved.