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Needle in a Haystack: Spotting and recognising micro-expressions "in the wild".
- Source :
-
Neurocomputing . Sep2022, Vol. 503, p283-298. 16p. - Publication Year :
- 2022
-
Abstract
- Computational research on facial micro-expressions has long focused on videos captured under constrained laboratory conditions due to the challenging elicitation process and limited samples that are publicly available. Moreover, processing micro-expressions is extremely challenging under unconstrained scenarios. This paper introduces, for the first time, a completely automatic micro-expression "spot-and-recognize" framework that is performed on in-the-wild videos, such as in poker games and political interviews. The proposed method first spots the apex frame from a video by handling head movements and unconscious actions which are typically larger in motion intensity, with alignment employed to enforce a canonical face pose. Optical flow guided features play a central role in our method: they can robustly identify the location of the apex frame, and are used to learn a shallow neural network model for emotion classification. Experimental results demonstrate the feasibility of the proposed methodology, establishing good baselines for both spotting and recognition tasks – ASR of 0.33 and F1-score of 0.6758 respectively on the MEVIEW micro-expression database. In addition, we present comprehensive qualitative and quantitative analyses to further show the effectiveness of the proposed framework, with new suggestion for an appropriate evaluation protocol. In a nutshell, this paper provides a new benchmark for apex spotting and emotion recognition in an in-the-wild setting. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 503
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 158185156
- Full Text :
- https://doi.org/10.1016/j.neucom.2022.06.101