Analysis of Micro-Expressions based on the Riesz Pyramid : Application to Spotting and Recognition

Abstract : 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 humans real intent. They have been studied to better understand non-verbal communications and in medical applications where is almost impossible to engage in a conversation or try to read the facial emotions or body language of a patient. There has been some interest works in micro-expression analysis, however, a great majority of these methods are based on classically established computer vision methods such as local binary patterns, histogram of gradients and optical flow. Considering the fact that this area of research is relatively new, much contributions remains to be made. ln this thesis, we present a novel methodology for subtle motion and micro-expression analysis. We propose to use the Riesz pyramid, a multi-scale steerable Hilbert transformer which has been used for 2-D phase representation and video amplification, as the basis for our methodology. For the general subtle motion analysis step, we transform an image sequence with the Riesz pyramid, extract and lifter the image phase variations as proxies for motion. Furthermore, we isolate regions of intcrcst where subtle motion might take place and mask noisy areas by thresholding the local amplitude. The total sequence is transformed into a ID signal which is used fo temporal analysis and subtle motion spotting. We create our own database of subtle motion sequences to test our method. For the micro-expression spotting step, we adapt the previous method to process some facial regions of interest. We also develop a heuristic method to detect facial micro-events that separates real micro-expressions from eye blinkings and subtle eye movements. For the micro-expression classification step, we exploit the dominant orientation constancy fom the Riesz transform to average the micro-expression sequence into an image pair. Based on that, we introduce the Mean Oriented Riesz Feature descriptor. The accuracy of our methods are tested in Iwo spontaneous micro-expressions databases. Furthermore, wc analyse the parameter variations and their effect in our results.
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Submitted on : Monday, October 28, 2019 - 12:02:11 PM
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Carlos Arango Duque. Analysis of Micro-Expressions based on the Riesz Pyramid : Application to Spotting and Recognition. Signal and Image Processing. Université de Lyon, 2018. English. ⟨NNT : 2018LYSES062⟩. ⟨tel-02335434⟩



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