It has been shown that the temporal evolution of the face during an expression is important to its correct interpretation. In recent years some approaches have attempt to model facial expressions dynamics by different methods such as HMMs, Dynamic Bayesian Networks or Bayesian temporal manifold model. In this paper, we present a novel approach to model the expression temporal dynamics using Gaussian Process Regression. Specifically, we use it to map the geometric features in any sequence frame into continuous values related with the intensity of the different types of expressions. Using the evolution of these values along the sequence, we are able to determine if the subject has performed any expression. In tests with the Cohn-Kanade database we show that this method can reach a higher performance than other existing methods with a 97.4% of accuracy.