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Facial Micro-Expression Analysis

Abstract : The Micro-expressions (MEs) are very important nonverbal communication clues. However, due to their local and short nature, spotting them is challenging. In this thesis, we address this problem by using a dedicated local and temporal pattern (LTP) of facial movement. This pattern has a specific shape (S-pattern) when ME are displayed. Thus, by using a classical classification algorithm (SVM), MEs are distinguished from other facial movements. We also propose a global final fusion analysis on the whole face to improve the distinction between ME (local) and head (global) movements. However, the learning of S-patterns is limited by the small number of ME databases and the low volume of ME samples. Hammerstein models (HMs) are known to be a good approximation of muscle movements. By approximating each S-pattern with a HM, we can both filter outliers and generate new similar S-patterns. By this way, we perform a data augmentation for S-pattern training dataset and improve the ability to differentiate MEs from other facial movements. In the first ME spotting challenge of MEGC2019, we took part in the building of the new result evaluation method. In addition, we applied our method to spotting ME in long videos and provided the baseline result for the challenge. The spotting results, performed on CASME I and CASME II, SAMM and CAS(ME)2, show that our proposed LTP outperforms the most popular spotting method in terms of F1-score. Adding the fusion process and data augmentation improve even more the spotting performance.
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Submitted on : Monday, June 22, 2020 - 4:29:35 PM
Last modification on : Monday, October 5, 2020 - 9:44:04 AM


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  • HAL Id : tel-02877766, version 1


Jingting Li. Facial Micro-Expression Analysis. Computer Vision and Pattern Recognition [cs.CV]. CentraleSupélec, 2019. English. ⟨NNT : 2019CSUP0007⟩. ⟨tel-02877766⟩



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