In this work we examine the use of State-Space Models to model the temporal information of dynamic facial expressions. The later being represented by the 3D animation parameters which are recovered using 3D Candide model. The 3D animation parameters of an image sequence can be seen as the observation of a stochastic process which can be modeled by a linear State-Space Model, the Kalman Filter. In the proposed approach each emotion is represented by a Kalman Filter, with parameters being State Transition matrix, Observation matrix, State and Observation noise covariance matrices. Person-independent experimental results have proved the validity and the good generalization ability of the proposed approach for emotional facial expression recognition. Moreover, compared to the state-of-the-art techniques, the proposed system yields significant improvements in recognizing facial expressions.