{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T04:27:54Z","timestamp":1729225674199,"version":"3.27.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>The vulnerability of individual-database learned model for Micro-Expression Recognition (MER) has significantly hindered their performance in real-world scenarios. Cross-Database Micro-Expression Recognition (CDMER) aims to enhance the robustness and generalization performance for more complicated situations. Most existing CDMER methods typically attempt to leverage spatial information to eliminate the inevitable domain shift between source and target domains. In this paper, we propose a novel feature space reconstruction model that provides feature space with better generalization for CDMER. Specifically, we introduce the Spatial and Frequency Domain Co-learning (SFDC), a three-branch module, that adaptively exploits the spatial and frequency characteristics of intermediate feature representations, capturing the global and local features of micro-expression adequately. Furthermore, to facilitate the synchronization of two domains, Source and Target Domain Synchronization (STDS) module is employed to guide the alignment of different subspaces simultaneously. Extensive experimental results on the SMIC and CASME II databases demonstrate the effectiveness of the reconstruction and the superiority of our proposed method over state-of-the-art (SOTA) methods.<\/jats:p>","DOI":"10.3233\/faia240538","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:47:26Z","timestamp":1729169246000},"source":"Crossref","is-referenced-by-count":0,"title":["Spatial and Frequency-Based Feature Reconstruction for Cross-Database Micro-Expression Recognition"],"prefix":"10.3233","author":[{"given":"Zhi","family":"Feng","sequence":"first","affiliation":[{"name":"South China University of Technology & Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human & Guangdong Provincial Key Laboratory of AI Large Model and Intelligent Cognition, Guangzhou, China"}]},{"given":"C. L. Philip","family":"Chen","sequence":"additional","affiliation":[{"name":"South China University of Technology & Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human & Guangdong Provincial Key Laboratory of AI Large Model and Intelligent Cognition, Guangzhou, China"},{"name":"Pazhou Lab, Guangzhou, China"}]},{"given":"Shiting","family":"Xu","sequence":"additional","affiliation":[{"name":"South China University of Technology & Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human & Guangdong Provincial Key Laboratory of AI Large Model and Intelligent Cognition, Guangzhou, China"},{"name":"Pazhou Lab, Guangzhou, China"}]},{"given":"Tong","family":"Zhang","sequence":"additional","affiliation":[{"name":"South China University of Technology & Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human & Guangdong Provincial Key Laboratory of AI Large Model and Intelligent Cognition, Guangzhou, China"},{"name":"Pazhou Lab, Guangzhou, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240538","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:47:26Z","timestamp":1729169246000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240538"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240538","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}