This paper describes a novel approach to building models of temporal dynamics for facial animation with applications in performing perceptual testing of trustworthiness. A vital component of the system is a method to bring two image sequences into temporal alignment. Our approach is to project the two sequences into face space (built using shape models [1]) and apply dynamic time warping (DTW). However, the variability in the sequences causes the standard DTW algorithm to perform poorly on our data, and so we have overcome this by extending DTW in the following ways: 1) the signal magnitudes are augmented by incorporating derivatives [2], and a scheme for estimating weights in the cost function is proposed, 2) the set of sequences is used to build a graph, with nodes representing sequences and edges indicating the cost of applying the extended DTW to align pairs of sequences; better alignments between sequences can now be found by traversing the minimum cost path through the graph. Once all signals are aligned to a common temporal reference it is straightforward to map the temporal dynamics from one face to another. A remapped face is synthesised using the new trajectory in face space to drive an active appearance model [1]. Furthermore, the common temporal reference allows us to build a statistical model of the dynamics. This can be used to both identify dynamics of interest and also to manipulate the dynamics, e.g. to reduce or exaggerate facial dynamics.