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Video Analysis for Micro- Expression Spotting and Recognition

Abstract : Recent years, there has been an increasing interest in the computer vision in automatic facial micro-expression algorithms. this has been driven by applications in high-stakes contexts such as criminal investigations, airport and mass transit checkpoints, counter terrorism, and so on. micro-expression approaches in computer vision area consist of detecting and classifying them from videos. compared to macro-expression, a micro-expression involves a rapid change which lasts less than a half of second, and moreover, its subtle appearance in part of the face makes detection and recognition difficult to achieve. effective facial features play a crucial role for micro-expression analysis. this thesis focuses on the feature extraction parts, by developing various feature extraction methods for types of micro-expression detection and recognition tasks.the detection of micro-expressions is the first step for its analysis. this thesis aims to spot micro-expressions from videos. existing detection methods based on features, such as the local binary patterns, the histogram of gradient, the optical flow suffer difficulties in computation consuming leading to real-time implementation problem. thus, in this thesis, the spotting method based on integral projection to address this problem. however, all the above features are extracted from cropped faces which usually cause residual mis-registration that appears between images. in order to deal with this issue, another detection method based on geometrical feature is proposed. it involves the geometrical distances between facial key-points without the need of cropping face. this captures subtle geometric displacements along sequences and is proved to be suitable for different facial analysis tasks that require high computational speed. for micro-expression recognition, motion features based on the optical flow have advantages in characterizing subtle movements on face among the existing recognition features. it is still a difficult problem for optical flow to determine the accurate locations of each facial feature mappings between different images even though the face images have been aligned. such an issue may give rise to wrong orientation and magnitude estimation associated to the optical flow field. in order to address this problem, the motion boundary histograms are considered. it can remove unexpected motions caused by residual mis-registration that appears between images cropped from different frames. nevertheless, the relative motion can be captured. based on the the motion boundary, a new descriptor the fusion motion boundary histograms is introduced. this feature is generated by combing both the horizontal and the vertical components of the differential of optical flow as inspired from the motion boundary histograms. the main contributions of this thesis lie at the study of features for micro-expressions spotting and recognition. experiments on the micro-expression databases show the effectiveness of the presented contributions.
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Submitted on : Friday, September 7, 2018 - 11:39:29 AM
Last modification on : Friday, October 23, 2020 - 4:48:53 PM
Long-term archiving on: : Saturday, December 8, 2018 - 2:23:24 PM


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  • HAL Id : tel-01870206, version 1


Hua Lu. Video Analysis for Micro- Expression Spotting and Recognition. Signal and Image processing. INSA de Rennes, 2018. English. ⟨NNT : 2018ISAR0005⟩. ⟨tel-01870206⟩



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