For a medical application, we are interested in an estimation of optical flow on a patient's face, particularly around the eyes. Among the methods of optical flow estimation, gradient estimation and block matching are the main methods. However, the gradient-based approach can only be applied for small displacements (one or two pixels). Generally, the process of block matching leads to good results only if the searching strategy is judiciously selected. Our approach is based on a Markov random field model, combined with an algorithm of block matching in a multiresolution scheme. The multiresolution approach allows detection of a large range of speeds. The large displacements are detected on coarse scales and small displacements are detected successively on finer scales in a coarse to fine strategy. The Markov random fields allow the initialization and control of motion estimation across all scales. The tracking of motion is achieved by a block matching algorithm. This method gives the optical flow, whatever the amplitude of motion is, if pertaining to the range defined by the multiresolution approach. The results clearly show the complement of Markov random field estimation and block matching across the scales.