{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T01:30:44Z","timestamp":1784511044031,"version":"3.55.0"},"reference-count":51,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T00:00:00Z","timestamp":1759017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development Program of the Autonomous Region","award":["2023B01005"],"award-info":[{"award-number":["2023B01005"]}]},{"name":"Key Research and Development Program of the Autonomous Region","award":["2022B01008"],"award-info":[{"award-number":["2022B01008"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022ZD0115802"],"award-info":[{"award-number":["2022ZD0115802"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62262065"],"award-info":[{"award-number":["62262065"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Entropy"],"abstract":"<jats:p>Micro-expressions are extremely subtle and short-lived facial muscle movements that often reveal an individual\u2019s genuine emotions. However, micro-expression recognition (MER) remains highly challenging due to its short duration, low motion intensity, and the imbalanced distribution of training samples. To address these issues, this paper proposes a Global\u2013Local Feature Fusion Network (GLFNet) to effectively extract discriminative features for MER. Specifically, GLFNet consists of three core modules: the Global Attention (LA) module, which captures subtle variations across the entire facial region; the Local Block (GB) module, which partitions the feature map into four non-overlapping regions to emphasize salient local movements while suppressing irrelevant information; and the Adaptive Feature Fusion (AFF) module, which employs an attention mechanism to dynamically adjust channel-wise weights for efficient global\u2013local feature integration. In addition, a class-balanced loss function is introduced to replace the conventional cross-entropy loss, mitigating the common issue of class imbalance in micro-expression datasets. Extensive experiments are conducted on three benchmark databases, SMIC, CASME II, and SAMM, under two evaluation protocols. The experimental results demonstrate that under the Composite Database Evaluation protocol, GLFNet consistently outperforms existing state-of-the-art methods in overall performance. Specifically, the unweighted F1-scores on the Combined, SAMM, CASME II, and SMIC datasets are improved by 2.49%, 2.02%, 0.49%, and 4.67%, respectively, compared to the current best methods. These results strongly validate the effectiveness and superiority of the proposed global\u2013local feature fusion strategy in micro-expression recognition tasks.<\/jats:p>","DOI":"10.3390\/e27101023","type":"journal-article","created":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T08:00:32Z","timestamp":1759132832000},"page":"1023","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["GLFNet: Attention Mechanism-Based Global\u2013Local Feature Fusion Network for Micro-Expression Recognition"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0212-1605","authenticated-orcid":false,"given":"Meng","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China"},{"name":"Xinjiang Key Laboratory of Multilingual Information Technology, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China"},{"name":"Xinjiang Key Laboratory of Multilingual Information Technology, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9351-8738","authenticated-orcid":false,"given":"Wenzhong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China"},{"name":"Xinjiang Key Laboratory of Multilingual Information Technology, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yabo","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China"},{"name":"Xinjiang Key Laboratory of Multilingual Information Technology, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1111\/j.1467-9280.2008.02116.x","article-title":"Reading between the lies: Identifying concealed and falsified emotions in universal facial expressions","volume":"19","author":"Porter","year":"2008","journal-title":"Psychol. Sci."},{"key":"ref_2","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_3","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_4","doi-asserted-by":"crossref","first-page":"128196","DOI":"10.1016\/j.neucom.2024.128196","article-title":"HTNet for micro-expression recognition","volume":"602","author":"Wang","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1109\/TAFFC.2016.2518162","article-title":"Microexpression identification and categorization using a facial dynamics map","volume":"8","author":"Xu","year":"2017","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Liong, S.T., Gan, Y.S., See, J., Khor, H.Q., and Huang, Y.C. (2019, January 14\u201318). Shallow triple stream three-dimensional cnn (ststnet) for micro-expression recognition. Proceedings of the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), Lille, France.","DOI":"10.1109\/FG.2019.8756567"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1109\/TPAMI.2007.1110","article-title":"Dynamic texture recognition using local binary patterns with an application to facial expressions","volume":"29","author":"Zhao","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Polikovsky, S., Kameda, Y., and Ohta, Y. (2009, January 3). Facial micro-expressions recognition using high speed camera and 3D-gradient descriptor. Proceedings of the 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009), London, UK.","DOI":"10.1049\/ic.2009.0244"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Shreve, M., Godavarthy, S., Goldgof, D., and Sarkar, S. (2011, January 21\u201323). Macro-and micro-expression spotting in long videos using spatio-temporal strain. Proceedings of the 2011 IEEE International Conference on Automatic Face & Gesture Recognition (FG), Santa Barbara, CA, USA.","DOI":"10.1109\/FG.2011.5771451"},{"key":"ref_10","unstructured":"Wang, S.J., Yan, W.J., Zhao, G., Fu, X., and Zhou, C.G. (2014, January 6\u201312). Micro-expression recognition using robust principal component analysis and local spatiotemporal directional features. Proceedings of the Computer Vision-ECCV 2014 Workshops, Zurich, Switzerland. Proceedings, Part I 13."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1109\/TIP.2020.3035042","article-title":"Joint local and global information learning with single apex frame detection for micro-expression recognition","volume":"30","author":"Li","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhai, Z., Zhao, J., Long, C., Xu, W., He, S., and Zhao, H. (2023, January 17\u201324). Feature representation learning with adaptive displacement generation and transformer fusion for micro-expression recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.02115"},{"key":"ref_13","unstructured":"Kumar, A.J.R., and Bhanu, B. (2021, January 19\u201325). Micro-expression classification based on landmark relations with graph attention convolutional network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108275","DOI":"10.1016\/j.patcog.2021.108275","article-title":"Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition","volume":"122","author":"Zhou","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.neucom.2021.03.063","article-title":"A comparative study on movement feature in different directions for micro-expression recognition","volume":"449","author":"Wei","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Davison, A.K., Merghani, W., and Yap, M.H. (2018). Objective classes for micro-facial expression recognition. J. Imaging, 4.","DOI":"10.3390\/jimaging4100119"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chaudhry, R., Ravichandran, A., Hager, G., and Vidal, R. (2009, January 20\u201325). Histograms of oriented optical flow and binet-cauchy kernels on nonlinear dynamical systems for the recognition of human actions. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206821"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1109\/TAFFC.2017.2723386","article-title":"Fuzzy histogram of optical flow orientations for micro-expression recognition","volume":"10","author":"Happy","year":"2017","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.image.2017.11.006","article-title":"Less is more: Micro-expression recognition from video using apex frame","volume":"62","author":"Liong","year":"2018","journal-title":"Signal Process. Image Commun."},{"key":"ref_20","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_21","unstructured":"Wang, Y., See, J., Phan, R.C.W., and Oh, Y.H. (2014, January 1\u20135). Lbp with six intersection points: Reducing redundant information in lbp-top for micro-expression recognition. Proceedings of the Computer Vision\u2014ACCV 2014: 12th Asian Conference on Computer Vision, Singapore. Revised Selected Papers, Part I 12."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1016\/j.neucom.2015.10.096","article-title":"Spontaneous facial micro-expression analysis using spatiotemporal completed local quantized patterns","volume":"175","author":"Huang","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, S.J., Yan, W.J., Li, X., Zhao, G., and Fu, X. (2014, January 24\u201328). Micro-expression recognition using dynamic textures on tensor independent color space. Proceedings of the 2014 22nd International Conference on Pattern Recognition, Stockholm, Sweden.","DOI":"10.1109\/ICPR.2014.800"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1109\/TMM.2019.2931351","article-title":"Spatiotemporal recurrent convolutional networks for recognizing spontaneous micro-expressions","volume":"22","author":"Xia","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Khor, H.Q., See, J., Liong, S.T., Phan, R.C., and Lin, W. (2019, January 22\u201325). Dual-stream shallow networks for facial micro-expression recognition. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8802965"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TMM.2022.3141616","article-title":"Block division convolutional network with implicit deep features augmentation for micro-expression recognition","volume":"25","author":"Chen","year":"2022","journal-title":"IEEE Trans. Multimed."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.neucom.2020.10.082","article-title":"GEME: Dual-stream multi-task GEnder-based micro-expression recognition","volume":"427","author":"Nie","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhao, S., Li, S., Zhang, Y., and Liu, S. (2025). Channel Self-Attention Residual Network: Learning Micro-Expression Recognition Features from Augmented Motion Flow Images. IEEE Trans. Affect. Comput., 1\u201316.","DOI":"10.1109\/TAFFC.2025.3568633"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.1109\/TAFFC.2023.3340016","article-title":"Geometric graph representation with learnable graph structure and adaptive au constraint for micro-expression recognition","volume":"15","author":"Wei","year":"2023","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hong, J., Lee, C., and Jung, H. (2022). Late fusion-based video transformer for facial micro-expression recognition. Appl. Sci., 12.","DOI":"10.3390\/app12031169"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.1109\/TAFFC.2022.3213509","article-title":"Short and long range relation based spatio-temporal transformer for micro-expression recognition","volume":"13","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lei, L., Chen, T., Li, S., and Li, J. (2021, January 19\u201325). Micro-expression recognition based on facial graph representation learning and facial action unit fusion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPRW53098.2021.00173"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"106258","DOI":"10.1016\/j.engappai.2023.106258","article-title":"C3DBed: Facial micro-expression recognition with three-dimensional convolutional neural network embedding in transformer model","volume":"123","author":"Pan","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2083","DOI":"10.1109\/TAFFC.2024.3397701","article-title":"Boosting Micro-Expression Recognition Via Self-Expression Reconstruction and Memory Contrastive Learning","volume":"15","author":"Bao","year":"2024","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"104537","DOI":"10.1016\/j.jvcir.2025.104537","article-title":"A multi-modal multi-scale network based on Transformer for micro-expression recognition","volume":"111","author":"Wang","year":"2025","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_37","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_38","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_39","doi-asserted-by":"crossref","first-page":"3160","DOI":"10.1109\/TMM.2018.2820321","article-title":"Learning from hierarchical spatiotemporal descriptors for micro-expression recognition","volume":"20","author":"Zong","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_40","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_41","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_42","unstructured":"Zach, C., Pock, T., and Bischof, H. (2007, January 12\u201314). A duality based approach for realtime tv-l 1 optical flow. Proceedings of the Pattern Recognition: 29th DAGM Symposium, Heidelberg, Germany. Proceedings 29."},{"key":"ref_43","unstructured":"Liu, Y., Shao, Z., and Hoffmann, N. (2021). Global attention mechanism: Retain information to enhance channel-spatial interactions. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"6544","DOI":"10.1109\/TIP.2021.3093397","article-title":"Learning deep global multi-scale and local attention features for facial expression recognition in the wild","volume":"30","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 14\u201319). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T.Y., Song, Y., and Belongie, S. (2019, January 15\u201320). Class-balanced loss based on effective number of samples. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00949"},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"1128","DOI":"10.3389\/fpsyg.2018.01128","article-title":"A survey of automatic facial micro-expression analysis: Databases, methods, and challenges","volume":"9","author":"Oh","year":"2018","journal-title":"Front. Psychol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Dollar, P. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/10\/1023\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T04:39:48Z","timestamp":1759207188000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/10\/1023"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,28]]},"references-count":51,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["e27101023"],"URL":"https:\/\/doi.org\/10.3390\/e27101023","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,28]]}}}