A wide variety of face models have been used in the recognition of full or micro facial expressions in image sequences. However, the existing methods only address one family of expression at a time, as micro-expressions are quite different from full-expressions in terms of facial movement amplitude and/or texture changes. In this paper we address the detection of micro and full-expression with a common facial model characterizing facial movements by means of consistent Optical Flow estimation. Optical Flow extracted from the face is generally noisy and without specific processing it can hardly cope with expression recognition requirements especially for micro-expressions. Direction and magnitude statistical profiles are jointly analyzed in order to filter out noise and obtain and feed consistent Optical Flows in a face motion model framework. Experiments on CK+ and CASME2 facial expression databases for full and micro expression recognition show the benefits brought by the proposed approach in the filed of facial expression recognition.