{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T20:25:19Z","timestamp":1769891119450,"version":"3.49.0"},"reference-count":45,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Postdoctoral Research Startup Fund Project of Shenzhen Polytechnic University","award":["6024331018K"],"award-info":[{"award-number":["6024331018K"]}]},{"name":"the National Key Research and Development Program of China","award":["2024YFE0216500"],"award-info":[{"award-number":["2024YFE0216500"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62573402"],"award-info":[{"award-number":["62573402"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Shenzhen Strategic Emerging Industry Support Plans","award":["XMHT20230115002"],"award-info":[{"award-number":["XMHT20230115002"]}]},{"name":"the Shenzhen Sustainable Development Sci-Tech project","award":["KCXFZ20230731093501003"],"award-info":[{"award-number":["KCXFZ20230731093501003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>With the growing development of facial micro-expression recognition technology, its practical application value has attracted increasing attention. In real-world scenarios, facial micro-expression recognition typically involves cross-dataset evaluation, where training and testing samples come from different datasets. Specifically, cross-dataset micro-expression recognition employs multi-dataset composite training and unseen single-dataset testing. This setup introduces two major challenges: inconsistent feature distributions across training sets and data imbalance. To address the distribution discrepancy of the same category across different training datasets, we propose a plug-and-play batch regularization learning module that constrains weight discrepancies across datasets through information-theoretic regularization, facilitating the learning of domain-invariant representations while preventing overfitting to specific source domains. To mitigate the data imbalance issue, we propose an Action Unit (AU)-guided generative adversarial network (GAN) for synthesizing micro-expression samples. This approach uses K-means clustering to obtain cluster centers of AU intensities for each category, which are then used to guide the GAN in generating balanced micro-expression samples. To validate the effectiveness of the proposed methods, extensive experiments are conducted on CNN, ResNet, and PoolFormer architectures. The results demonstrate that our approach achieves superior performance in cross-dataset recognition compared to state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/e28020150","type":"journal-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T13:29:03Z","timestamp":1769693343000},"page":"150","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-Dataset Facial Micro-Expression Recognition with Regularization Learning and Action Unit-Guided Data Augmentation"],"prefix":"10.3390","volume":"28","author":[{"given":"Ju","family":"Zhou","sequence":"first","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"Tech X Academy, Shenzhen Polytechnic University, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Guangdong University of Education, Guangzhou 510303, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9111-2723","authenticated-orcid":false,"given":"Lin","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"Shenzhen Lower Limb Intelligent Rehabilitation Engineering Research Center, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"Tech X Academy, Shenzhen Polytechnic University, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haolin","family":"Xia","sequence":"additional","affiliation":[{"name":"Hong Kong Center for Construction Robotics, Hong Kong 999077, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pfister, T., Li, X., Zhao, G., and Pietik\u00e4inen, M. (2011, January 6\u201313). Recognising spontaneous facial micro-expressions. Proceedings of the 2011 International Conference On Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126401"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1109\/TAFFC.2017.2667642","article-title":"Towards reading hidden emotions: A comparative study of spontaneous micro-expression spotting and recognition methods","volume":"9","author":"Li","year":"2017","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Malik, P., Singh, J., Ali, F., Sehra, S.S., and Kwak, D. (2025). Action unit based micro-expression recognition framework for driver emotional state detection. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-12245-7"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Shuai, T., Beng, S., Khalid, F.B., and Rahmat, R.W.B.O. (2025). Advances in Facial Micro-Expression Detection and Recognition: A Comprehensive Review. Information, 16.","DOI":"10.3390\/info16100876"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2028","DOI":"10.1109\/TAFFC.2022.3205170","article-title":"Deep learning for micro-expression recognition: A survey","volume":"13","author":"Li","year":"2022","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yang, H., Xie, L., Pan, H., Li, C., Wang, Z., and Zhong, J. (2023). Multimodal Attention Dynamic Fusion Network for Facial Micro-Expression Recognition. Entropy, 25.","DOI":"10.3390\/e25091246"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Gu, S., Sun, X., Chen, B., and Tao, W. (2024). Depression Micro-Expression Recognition Technology Based on Multimodal Knowledge Graphs. Trait. Signal, 41.","DOI":"10.18280\/ts.410433"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1109\/TAFFC.2022.3143100","article-title":"An overview of facial micro-expression analysis: Data, methodology and challenge","volume":"14","author":"Xie","year":"2022","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Maryala, S., Kommula, V., Kumarapu, S., and A, K. (2025, January 5\u20137). Micro-expression Analysis for Security and Law Enforcment using Deep Learning. Proceedings of the 2025 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India.","DOI":"10.1109\/ESCI63694.2025.10988116"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, X., Pfister, T., Huang, X., Zhao, G., and Pietik\u00e4inen, M. (2013, January 22\u201326). A spontaneous micro-expression database: Inducement, collection and baseline. Proceedings of the 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Shanghai, China.","DOI":"10.1109\/FG.2013.6553717"},{"key":"ref_11","unstructured":"Yan, W.J., Wu, Q., Liu, Y.J., Wang, S.J., and Fu, X. (2013, January 22\u201326). CASME database: A dataset of spontaneous micro-expressions collected from neutralized faces. Proceedings of the 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Shanghai, China."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yan, W.J., Li, X., Wang, S.J., Zhao, G., Liu, Y.J., Chen, Y.H., and Fu, X. (2014). CASME II: An improved spontaneous micro-expression database and the baseline evaluation. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0086041"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/TAFFC.2016.2573832","article-title":"SAMM: A spontaneous micro-facial movement dataset","volume":"9","author":"Davison","year":"2016","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_14","first-page":"5826","article-title":"Video-based facial micro-expression analysis: A survey of datasets, features and algorithms","volume":"44","author":"Ben","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhou, L., Mao, Q., and Xue, L. (2019, January 8\u201312). Cross-database micro-expression recognition: A style aggregated and attention transfer approach. Proceedings of the 2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Shanghai, China.","DOI":"10.1109\/ICMEW.2019.00025"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"See, J., Yap, M.H., Li, J., Hong, X., and Wang, S.J. (2019, January 14\u201318). Megc 2019\u2014The second facial micro-expressions grand challenge. Proceedings of the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), Lille, France.","DOI":"10.1109\/FG.2019.8756611"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yap, M.H., See, J., Hong, X., and Wang, S.J. (2018, January 15\u201319). Facial micro-expressions grand challenge 2018 summary. Proceedings of the 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi\u2019an, China.","DOI":"10.1109\/FG.2018.00106"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, Y., Du, H., Zheng, L., and Gedeon, T. (2019, January 14\u201318). A neural micro-expression recognizer. Proceedings of the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), Lille, France.","DOI":"10.1109\/FG.2019.8756583"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zong, Y., Huang, X., Zheng, W., Cui, Z., and Zhao, G. (2017, January 23\u201327). Learning a target sample re-generator for cross-database micro-expression recognition. Proceedings of the 25th ACM International Conference on Multimedia, Mountain View, CA, USA.","DOI":"10.1145\/3123266.3123367"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1109\/TAFFC.2023.3265063","article-title":"Data leakage and evaluation issues in micro-expression analysis","volume":"15","author":"Varanka","year":"2023","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1007\/s10489-023-05213-z","article-title":"ULME-GAN: A generative adversarial network for micro-expression sequence generation","volume":"54","author":"Zhou","year":"2024","journal-title":"Appl. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1109\/TCDS.2022.3226348","article-title":"Deep insights of learning-based micro expression recognition: A perspective on promises, challenges, and research needs","volume":"15","author":"Verma","year":"2022","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1007\/s00371-018-1607-6","article-title":"Micro-expression recognition: An updated review of current trends, challenges and solutions","volume":"36","author":"Goh","year":"2020","journal-title":"Vis. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhou, L., Mao, Q., and Xue, L. (2019, January 14\u201318). Dual-inception network for cross-database micro-expression recognition. Proceedings of the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), Lille, France.","DOI":"10.1109\/FG.2019.8756579"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wei, M., Zong, Y., Jiang, X., Lu, C., and Liu, J. (2022). Micro-Expression Recognition Using Uncertainty-Aware Magnification-Robust Networks. Entropy, 24.","DOI":"10.3390\/e24091271"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Peng, M., Wu, Z., Zhang, Z., and Chen, T. (2018, January 15\u201319). From macro to micro expression recognition: Deep learning on small datasets using transfer learning. Proceedings of the 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi\u2019an, China.","DOI":"10.1109\/FG.2018.00103"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yu, J., Zhang, C., Song, Y., and Cai, W. (2021, January 18\u201322). ICE-GAN: Identity-aware and capsule-enhanced GAN with graph-based reasoning for micro-expression recognition and synthesis. Proceedings of the 2021 International joint conference on neural networks (IJCNN), Shenzhen, China.","DOI":"10.1109\/IJCNN52387.2021.9533988"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, M., Yao, L., Wenzhong, Y., and Yin, Y. (2025). GLFNet: Attention Mechanism-Based Global\u2013Local Feature Fusion Network for Micro-Expression Recognition. Entropy, 27.","DOI":"10.3390\/e27101023"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gan, C., Xiao, J., Zhu, Q., and Zhu, Y. (2025). Macro-expression-guided micro-expression recognition: A motion similarity perspective. Pattern Recognit., 171.","DOI":"10.1016\/j.patcog.2025.112237"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, M., Yang, W., Wang, L., Wu, Z., and Chen, D. (2025). HFA-Net: Hierarchical feature aggregation network for micro-expression recognition. Complex Intell. Syst., 11.","DOI":"10.1007\/s40747-025-01804-0"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Peng, M., Wang, C., Bi, T., Shi, Y., Zhou, X., and Chen, T. (2019, January 3\u20136). A novel apex-time network for cross-dataset micro-expression recognition. Proceedings of the 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII), Cambridge, UK.","DOI":"10.1109\/ACII.2019.8925525"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1998","DOI":"10.1109\/TAFFC.2022.3197785","article-title":"Objective class-based micro-expression recognition under partial occlusion via region-inspired relation reasoning network","volume":"13","author":"Mao","year":"2022","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1109\/TKDE.2020.2985365","article-title":"Cross-Database Micro-Expression Recognition: A Benchmark","volume":"34","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2484","DOI":"10.1109\/TIP.2018.2797479","article-title":"Domain regeneration for cross-database micro-expression recognition","volume":"27","author":"Zong","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, Z., Fu, W., Zhang, Y., Li, J., Gong, W., and Gonz\u00e0lez, J. (2024). MCNet: Meta-clustering learning network for micro-expression recognition. J. Electron. Imaging, 33.","DOI":"10.1117\/1.JEI.33.2.023014"},{"key":"ref_36","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, K., Peng, X., Yang, J., Lu, S., and Qiao, Y. (2020, January 13\u201319). Suppressing uncertainties for large-scale facial expression recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00693"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Baltru\u0161aitis, T., Mahmoud, M., and Robinson, P. (2015, January 4\u20138). Cross-dataset learning and person-specific normalisation for automatic action unit detection. Proceedings of the 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Ljubljana, Slovenia.","DOI":"10.1109\/FG.2015.7284869"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yu, W., Luo, M., Zhou, P., Si, C., Zhou, Y., Wang, X., Feng, J., and Yan, S. (2022, January 18\u201324). MetaFormer is Actually What You Need for Vision. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01055"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Teed, Z., and Deng, J. (2020, January 23\u201328). RAFT: Recurrent All-Pairs Field Transforms for Optical Flow. Proceedings of the European Conference on Computer Vision (ECCV), Glasgow, UK.","DOI":"10.1007\/978-3-030-58536-5_24"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.neucom.2020.06.005","article-title":"Micro-Attention for Micro-Expression recognition","volume":"410","author":"Wang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Talluri, K.K., Fiedler, M.A., and Al-Hamadi, A. (2022). Deep 3d Convolutional Neural Network for Facial Micro-Expression Analysis from Video Images. Appl. Sci., 12.","DOI":"10.3390\/app122111078"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, M., Huan, Z., and Shang, L. (2020, January 16\u201318). Micro-Expression Recognition Using Micro-Variation Boosted Heat Areas. Proceedings of the Chinese Conference on Pattern Recognition and Computer Vision, Nanjing, China.","DOI":"10.1007\/978-3-030-60639-8_44"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Khor, H.Q., See, J., Phan, R.C.W., and Lin, W. (2018, January 15\u201319). Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition. Proceedings of the 13th IEEE International Conference on Automatic Face & Gesture Recognition, Xi\u2019an, China.","DOI":"10.1109\/FG.2018.00105"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/2\/150\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T05:12:51Z","timestamp":1769836371000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/2\/150"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,29]]},"references-count":45,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["e28020150"],"URL":"https:\/\/doi.org\/10.3390\/e28020150","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,29]]}}}