Unsupervised cross-database micro-expression recognition based on distribution adaptation

Autor: Bing Li, Ying Zhou, Ruixue Xiao, Jianchao Wang, Xianye Ben, Kidiyo Kpalma, Hongchao Zhou
Přispěvatelé: Shandong University, Institut d'Electronique et de Télécommunications de Rennes (IETR), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Ecole Supérieure d'Electricité - SUPELEC (FRANCE)-Centre National de la Recherche Scientifique (CNRS), Natural Science Foundation of China National Natural Science Foundation of China (NSFC) [61571275, 61971468], Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project) [2019JZZY010119]
Rok vydání: 2022
Předmět:
Zdroj: Multimedia Systems
Multimedia Systems, 2022, 28 (3), pp.1099-1116. ⟨10.1007/s00530-022-00896-9⟩
ISSN: 1432-1882
0942-4962
DOI: 10.1007/s00530-022-00896-9
Popis: International audience; Different from the traditional macro-expressions, micro-expressions are unconscious, quick and trustworthy facial expressions, which can reveal real emotion. Micro-expressions can provide information that is important and crucial in applications such as lie detection, criminal investigation, pain or mood assessment, etc. However, it is worth noting that most current micro-expression recognition methods rely only on a single micro-expression database. If the training and test samples belong to different domains, for example, different micro-expression databases, the accuracy of existing micro-expression recognition methods will decrease dramatically. To solve this problem, we propose an unsupervised cross-database micro-expression recognition method based on distribution adaptation. Compared with most advanced unsupervised cross-database recognition methods, the proposed method has better performance on micro-expression recognition tasks.
Databáze: OpenAIRE
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