AU-guided Feature Aggregation for Micro-Expression Recognition

AU-guided Feature Aggregation for Micro-Expression Recognition | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Computer Animation and Virtual Worlds This is a preprint and has not been peer reviewed. Data may be preliminary. 28 April 2025 V1 Latest version Share on AU-guided Feature Aggregation for Micro-Expression Recognition Authors : Xiaohui Tan 0000-0002-9160-3813 , Weiqi Xu , Jiazheng Wu , Hao Geng , and Qichuan Geng [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174582245.51052169/v1 Published Computer Animation and Virtual Worlds Version of record Peer review timeline 331 views 223 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Micro-expressions (MEs) are spontaneous and transient facial movements that reflect real internal emotions and have been widely applied in various fields. Recent deep learning-based methods have been rapidly developing in micro-expression recognition (MER).Still,it is typical to focus on the one-sided nature of MEs, covering only representational features or low-ranking Action Unit (AU) features. The subtle changes in MEs characterize its feature representation weak and inconspicuous,making it tough to analyze MEs only from a single piece or a small amount of information to achieve a considerable recognition effect. In addition, the lower-order information can only distinguish MEs from a single low-dimensional perspective and neglects the potential of corresponding MEs and AU combinations to each other. To address these issues, we first explore how the higher-order relations of different AU combinations correspond with MEs through statistical analysis. Afterward, based on this attribute, we propose an end-to-end multi-stream model that integrates global feature learning and local muscle movement representation guided by AU semantic information. The comparative experiments were performed on benchmark datasets, with better performance than the state-of-art methods. Also, the ablation experiments demonstrate the necessity of our model to introduce the information of AU and its relationship to MER. Supplementary Material File (au_guided_feature_aggregation_for_micro_expression_recognition.pdf) Download 10.07 MB Information & Authors Information Version history V1 Version 1 28 April 2025 Peer review timeline Published Computer Animation and Virtual Worlds Version of Record 17 Jun 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Computer Animation and Virtual Worlds Keywords action unit high-level relation micro expression recognition spatial-temporal Authors Affiliations Xiaohui Tan 0000-0002-9160-3813 Capital Normal University View all articles by this author Weiqi Xu Capital Normal University View all articles by this author Jiazheng Wu Capital Normal University View all articles by this author Hao Geng Capital Normal University View all articles by this author Qichuan Geng [email protected] Capital Normal University View all articles by this author Metrics & Citations Metrics Article Usage 331 views 223 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Xiaohui Tan, Weiqi Xu, Jiazheng Wu, et al. AU-guided Feature Aggregation for Micro-Expression Recognition. Authorea . 28 April 2025. DOI: https://doi.org/10.22541/au.174582245.51052169/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); Cited by Anandhavalli Muniasamy, Rizwan Abbas, Galiya Ybytayeva, Ashwag Alasmari, Nouf Aldahwan, Hend Khalid Alkahtani, Attention-enhanced CNN-LSTM framework for real-time video-based emotion recognition, The Visual Computer, 42 , 5, (2026). https://doi.org/10.1007/s00371-026-04396-z Crossref Loading... 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