Mean Oriented Riesz Features for Micro Expression Classification
This addresses micro-expression recognition for applications like lie detection or emotion analysis, but it is incremental as it builds on existing computer vision techniques with a novel feature description.
The paper tackles micro-expression classification by introducing Mean Oriented Riesz Features, which use the Riesz pyramid to extract motion proxies from image sequences and average them into an image pair, achieving performance tested on two spontaneous databases and compared to state-of-the-art methods.
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.