{"id":12344,"date":"2026-09-25T15:54:35","date_gmt":"2026-09-25T07:54:35","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=12344"},"modified":"2026-09-25T20:24:04","modified_gmt":"2026-09-25T12:24:04","slug":"jase-202612-35-067","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202612-35-067","title":{"rendered":"MSDF-Net: Macro-Guided Static\u2013Dynamic Fusion for Micro Expression Recognition"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 35<\/a><\/p>\n<\/div>\n\n\n\n

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Yan Lv, Nannan Hu, Fan Zhou<\/i><\/a>, Yang Wang<\/p>\n\n\n\n

Shenyang Ligong University, Shenyang, Liaoning 110159, China<\/p>\n<\/div>\n\n\n\n

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Received: July 29, 2026
Accepted: September 11, 2026
Publication Date: September 25, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Confusion matrices of MSDF-Net under the three-class setting on CASME II, SAMM, and SMIC-HS. <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202612_35.067<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Micro-expression recognition is challenging because facial changes are brief and weak, training sets are small and imbalanced, and optical flow is vulnerable to alignment and non-expression motion. This paper proposes MSDF-Net, a subject-independent macro-guided static-dynamic recognition system. An apex-ADM branch captures appearance and local onset-apex variation, while a shared optical-flow encoder represents micro- and macro-expression motion. Unlike macro-guided methods that directly concatenate or transfer macro features, MSDF-Net converts a motion-matched macro feature into adaptive channel weights and applies them residually to the target micro-expression representation, exploiting strong macro motion while limiting contamination caused by intensity mismatch. Static-guided cross-modal attention then integrates recalibrated motion with static cues, and a similarity-weighted uncertainty-aware objective reduces the influence of imperfect macro-micro correspondence. Under LOSO evaluation, MSDF-Net obtains UF1 scores of 0.9962 and 0.9591 on the three- and five-class CASME II tasks, 0.9545 and 0.8612 on the corresponding SAMM tasks, and 0.8990 on SMIC-HS. Controlled comparisons and ablations support the contributions of complementary representation, macro-guided recalibration, and cross-modal fusion. On ECMSE, the model achieves 0.8471 Accuracy and 0.8435 UF1, indicating preliminary cross-scenario applicability. The results demonstrate that selective macro guidance and complementary static-dynamic modeling improve subject-independent micro-expression recognition.<\/p>\n\n\n\n

Keywords: Micro-expression recognition; Static-dynamic fusion; Macro-guided recalibration; Channel recalibration; Cross-modal attention<\/em><\/p>\n\n\n\n

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