Abstract: The recognition of micro- has been a great challenge for its three characteristics, i.e., short duration, low intensity and usually local movements. This paper proposed a novel Mean Histogram of Oriented Optical Flow (MHOOF) feature for micro- recognition. First, a set of facial feature landmarks were detected and 13 Regions of Interest (ROIs) were partitioned in facial area based on the landmark coordinates and Facial Action Coding System (FACS), then the apex frame was detected by HOOF feature extracted in some specific ROIs frame-by-frame. Finally, MHOOF features were extracted from the image sequence that from the onset frame to the apex frame for recognition. The experimental results on the ideal spontaneous micro- database, namely, CASME II indicate that the proposed method can describe the changes of micro- effectively, and improvements of 5.53% and 3.12% are achieved when compared to the two state-of-the-art algorithms MDMO and DiSTLBP-RIP respectively.