  Micro-expressions are brief and subtle facial expressions that go on and off
the face in a fraction of a second. This kind of facial expressions usually
occurs in high stake situations and is considered to reflect a human's real
intent. There has been some interest in micro-expression analysis, however, a
great majority of the methods are based on classically established computer
vision methods such as local binary patterns, histogram of gradients and
optical flow. A novel methodology for micro-expression recognition using the
Riesz pyramid, a multi-scale steerable Hilbert transform is presented. In fact,
an image sequence is transformed with this tool, then the image phase
variations are extracted and filtered as proxies for motion. Furthermore, the
dominant orientation constancy from the Riesz transform is exploited to average
the micro-expression sequence into an image pair. Based on that, the Mean
Oriented Riesz Feature description is introduced. Finally the performance of
our methods are tested in two spontaneous micro-expressions databases and
compared to state-of-the-art methods.
