We present a particle filter algorithm to optimize the static shape parameters of a given face observed under multiple views and during time. Our goal is to determine the 3D shape of the head given these observations, by selecting the most suitable deformation parameters. The main idea of our method is to integrate the unknown static parameters in the particle filter hidden state and to filter and modify these parameter values given the recursively incoming observations. We propose here a comparative study of different variants of this approach evaluated on synthetic data. These results show the potential given by this type of particle based methods, which have mainly been presented from a theoretical point of view until now. We conclude with a discussion on the adaptation of these methods to real data sequences.