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Currently, MER technology is advancing rapidly; however, several challenges remain. To address the insufficient capture of local features and high computational complexity in MER, this paper proposes a novel lightweight network architecture. We introduce a Hierarchical Interactive Multi\u2010scale Pyramid Network (HIM\u2010PyraNet) that identifies key facial muscle movement regions while considering inherent spatial relationships between facial landmarks, differing from most existing studies using self\u2010attention mechanisms. HIM\u2010PyraNet comprises two main components: a Cross\u2010Region Interaction Attention (CRIA) module focusing on local temporal features and a Multi\u2010scale Feature Pyramid Fusion (MFPF) module integrating local and global semantics. Specifically, the face is divided into four distinct regions: left eye, right eye, left lip, and right lip. The CRIA module captures local micro\u2010muscle movements with region\u2010specific self\u2010attention, while the MFPF module learns interactions between eye and lip regions. This strategy effectively reduces the processing area for facial MER, successfully builds facial regional collaboration, and retains the local detailed features of expressions while reducing computational parameters. Experiments on SAMM, CASME II, and SMIC datasets show that with only 0.25M parameters, our model achieves 85.31% average recognition accuracy and 75.3 frames per second (FPS) inference speed. Compared with existing methods, this architecture demonstrates higher accuracy with a low parameter count, providing an innovative solution for real\u2010time micro\u2010expression analysis on edge devices.<\/jats:p>","DOI":"10.1002\/cpe.70487","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T11:22:01Z","timestamp":1764760921000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HIM\u2010PyraNet: Hierarchical Attention and Region\u2010Focused Lightweight Network for Micro\u2010Expression Recognition"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6594-1545","authenticated-orcid":false,"given":"Fangjie","family":"Xue","sequence":"first","affiliation":[{"name":"Information Engineering College Capital Normal University  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongxiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Information Engineering College Capital Normal University  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuhong","family":"Shao","sequence":"additional","affiliation":[{"name":"Information Engineering College Capital Normal University  Beijing China"},{"name":"Hebei Key Laboratory of Digital Physical Fitness Monitoring and Health Promotion Hebei Institute of Sports Science  Shijiazhuang China"},{"name":"Jiaxing Key Laboratory of Smart Transportations  Jiaxing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanlong","family":"Cui","sequence":"additional","affiliation":[{"name":"Information Engineering College Capital Normal University  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zehao","family":"Yuan","sequence":"additional","affiliation":[{"name":"Information Engineering College Capital Normal University  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,12,3]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0086041"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00149"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02115"},{"key":"e_1_2_9_5_1","unstructured":"j J.Mao R.Xu X.Yin Y.Chang B.Nie andA.Huang \u201cPOSTER V2: A Simpler and Stronger Facial Expression Recognition Network \u201d(2023). 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