Mean Oriented Riesz Features for Micro Expression Classification

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

Simultaneous magnification of subtle motions and color variations in videos using riesz pyramids

Videos often contain subtle motions and color variations that cannot be easily observed. Examples include, for instance, head motion and changes in skin face color due to blood flow controlled by the heart pumping rhythm. A few techniques have been developed to magnify these subtle signals. However, they are not easily applied to many applications. First of all, previous techniques were targeted specifically towards magnification of either motion or color variations. Trying to magnify both aspects applying two of these tech niques in sequence does not produce good results. We present a method for magnifying subtle motions and color variations in videos simultaneously. Our approach is based on the Riesz pyramid, which was previously used only for motion magnification. Besides modifying the local phases of the coefficients of this pyramid, we show how altering its local amplitudes and its residue can be used to produce intensity (color) magnification. We demonstrate the effectiveness of our technique in multiple videos by revealing both subtle signals simultaneously. Finally, we also developed an Android application as a proof-of-concept that can be used for magnifying either motion or color changes

Subtle Motion Analysis and Spotting using the Riesz Pyramid

International audienceAnalyzing and temporally spotting motions which are almost invisible to the human eye might reveal interesting information about the world. However, detecting these events is difficult due to their short duration and low intensities. Taking inspiration from video magnification techniques, we design a workflow for analyzing and temporally spotting subtle motions based on the Riesz pyramid. In addition, we propose a filtering and masking scheme that segments motions of interest without producing undesired artifacts or delays. In order to be able to evaluate the spotting accuracy of our method, we introduce our own database containing videos of subtle motions. Experiments are carried out under different types and levels of noise. Finally, we show that our method is able to outperform other state of the art methods in this challenging task