{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T09:27:16Z","timestamp":1775726836391,"version":"3.50.1"},"reference-count":83,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,5,2]],"date-time":"2021-05-02T00:00:00Z","timestamp":1619913600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Facial expressions provide important information concerning one\u2019s emotional state. Unlike regular facial expressions, microexpressions are particular kinds of small quick facial movements, which generally last only 0.05 to 0.2 s. They reflect individuals\u2019 subjective emotions and real psychological states more accurately than regular expressions which can be acted. However, the small range and short duration of facial movements when microexpressions happen make them challenging to recognize both by humans and machines alike. In the past decade, automatic microexpression recognition has attracted the attention of researchers in psychology, computer science, and security, amongst others. In addition, a number of specialized microexpression databases have been collected and made publicly available. The purpose of this article is to provide a comprehensive overview of the current state of the art automatic facial microexpression recognition work. To be specific, the features and learning methods used in automatic microexpression recognition, the existing microexpression data sets, the major outstanding challenges, and possible future development directions are all discussed.<\/jats:p>","DOI":"10.3390\/make3020021","type":"journal-article","created":{"date-parts":[[2021,5,2]],"date-time":"2021-05-02T08:05:21Z","timestamp":1619942721000},"page":"414-434","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Review of Automatic Microexpression Recognition in the Past Decade"],"prefix":"10.3390","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1860-5281","authenticated-orcid":false,"given":"Liangfei","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science, University of St Andrews, St Andrews KY16 9SX, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9314-194X","authenticated-orcid":false,"given":"Ognjen","family":"Arandjelovi\u0107","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of St Andrews, St Andrews KY16 9SX, UK"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1146\/annurev.ps.30.020179.002523","article-title":"Facial Expressions of Emotion","volume":"30","author":"Ekman","year":"1979","journal-title":"Annu. Rev. Psychol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1111\/1467-9280.00221","article-title":"Unconscious facial reactions to emotional facial expressions","volume":"11","author":"Dimberg","year":"2000","journal-title":"Psychol. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Haggard, E.A., and Isaacs, K.S. (1966). Micromomentary facial expressions as indicators of ego mechanisms in psychotherapy. Methods of Research in Psychotherapy, Springer.","DOI":"10.1007\/978-1-4684-6045-2_14"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1080\/00332747.1969.11023575","article-title":"Nonverbal Leakage and Clues to Deception","volume":"32","author":"Ekman","year":"1969","journal-title":"Psychiatry"},{"key":"ref_5","unstructured":"Ekman, P. (2011). Lie Catching and Microexpressions. The Philosophy of Deception, Oxford University Press."},{"key":"ref_6","first-page":"1","article-title":"How emotions work: The social functions of emotional expression in negotiations","volume":"22","author":"Morris","year":"2000","journal-title":"Res. Organ. Behav."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1109\/TAFFC.2014.2316163","article-title":"The faces of engagement: Automatic recognition of student engagement from facial expressions","volume":"5","author":"Whitehill","year":"2014","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_8","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_9","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_10","unstructured":"Rani, M., and Rathee, N. (2020, January 29\u201330). Microexpression Analysis: A Review. Proceedings of the 3rd International Conference on Computing Informatics and Networks: ICCIN 2020, Delhi, India."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.vrih.2020.10.003","article-title":"Review of micro-expression spotting and recognition in video sequences","volume":"3","author":"Pan","year":"2021","journal-title":"Virtual Real. Intell. Hardw."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Polikovsky, S., Kameda, Y., and Ohta, Y. (2009). Facial Micro-Expressions Recognition Using High Speed Camera and 3D-Gradient Descriptor, IET. IET Seminar Digest.","DOI":"10.1049\/ic.2009.0244"},{"key":"ref_13","unstructured":"Ekman, P., and Friesen, W.V. (1971). Facial Action Coding System: A Technique for the Measurement of Facial Movement, Consulting Psychologists Press."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/0031-3203(95)00067-4","article-title":"A comparative study of texture measures with classification based on featured distributions","volume":"29","author":"Ojala","year":"1996","journal-title":"Pattern Recognit."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fan, J., and Arandjelovi\u0107, O. (2018, January 8\u201313). Employing domain specific discriminative information to address inherent limitations of the LBP descriptor in face recognition. Proceedings of the International Joint Conference on Neural Networks, Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489691"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Karsten, J., and Arandjelovi\u0107, O. (2017, January 11\u201315). Automatic vertebrae localization from CT scans using volumetric descriptors. Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society, Jeju, Korea.","DOI":"10.1109\/EMBC.2017.8036890"},{"key":"ref_17","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_18","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 IEEE International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126401"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1006\/cviu.1995.1004","article-title":"Active shape models-their training and application","volume":"61","author":"Cootes","year":"1995","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/0262-8856(88)90016-9","article-title":"Image registration by local approximation methods","volume":"6","author":"Goshtasby","year":"1988","journal-title":"Image Vis. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.neucom.2016.05.083","article-title":"Sparse tensor canonical correlation analysis for micro-expression recognition","volume":"214","author":"Wang","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, Y., See, J., Raphael, R., and Oh, Y.H. (2015). LBP with six intersection points: Reducing redundant information in LBP-TOP for micro-expression recognition. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer.","DOI":"10.1007\/978-3-319-16865-4_34"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Fu, X., and Wei, W. (2008, January 18\u201320). Centralized binary patterns embedded with image euclidean distance for facial expression recognition. Proceedings of the 4th International Conference on Natural Computation, ICNC 2008, Jinan, China.","DOI":"10.1109\/ICNC.2008.94"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4446","DOI":"10.1016\/j.ijleo.2015.08.167","article-title":"Micro-expression recognition based on CBP-TOP feature with ELM","volume":"126","author":"Guo","year":"2015","journal-title":"Optik"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2499","DOI":"10.1016\/j.patcog.2012.01.013","article-title":"Colour invariants under a non-linear photometric camera model and their application to face recognition from video","volume":"45","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_26","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 International Conference on Pattern Recognition, Stockholm, Sweden.","DOI":"10.1109\/ICPR.2014.800"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6034","DOI":"10.1109\/TIP.2015.2496314","article-title":"Micro-Expression Recognition Using Color Spaces","volume":"24","author":"Wang","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, L., Arandjelovi\u0107, O., Dewar, S., Astell, A., Doherty, G., and Ellis, M. (2020;, January 20\u201324). Quantification of advanced dementia patients\u2019 engagement in therapeutic sessions: An automatic video based approach using computer vision and machine learning. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9176632"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1109\/TAFFC.2015.2485205","article-title":"A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition","volume":"7","author":"Liu","year":"2016","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Asthana, A., Zafeiriou, S., Cheng, S., and Pantic, M. (2013, January 23\u201328). Robust discriminative response map fitting with constrained local models. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.442"},{"key":"ref_31","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 Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, Miami, FL, USA.","DOI":"10.1109\/CVPRW.2009.5206821"},{"key":"ref_32","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_33","doi-asserted-by":"crossref","unstructured":"Kim, D.H., Baddar, W.J., and Ro, Y.M. (2016, January 15\u201319). Micro-expression recognition with expression-state constrained spatio-temporal feature representations. Proceedings of the MM 2016\u2014Proceedings of the 2016 ACM Multimedia Conference, Amsterdam, The Netherlands.","DOI":"10.1145\/2964284.2967247"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.3389\/fpsyg.2017.01745","article-title":"Dual temporal scale convolutional neural network for micro-expression recognition","volume":"8","author":"Peng","year":"2017","journal-title":"Front. Psychol."},{"key":"ref_35","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":"2015","journal-title":"Neurocomputing"},{"key":"ref_36","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 and Gesture Recognition, FG 2018, Xi\u2019an, China.","DOI":"10.1109\/FG.2018.00105"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Donahue, J., Hendricks, L.A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., and Saenko, K. (2015, January 7\u201312). Long-term recurrent convolutional networks for visual recognition and description. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298878"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Shreve, M., Godavarthy, S., Goldgof, D., and Sarkar, S. (2011, January 21\u201325). Macro- and micro-expression spotting in long videos using spatio-temporal strain. Proceedings of the 2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011, Santa Barbara, CA, USA.","DOI":"10.1109\/FG.2011.5771451"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s10919-008-0057-7","article-title":"Detecting deception from emotional and unemotional cues","volume":"33","author":"Warren","year":"2009","journal-title":"J. Nonverbal Behav."},{"key":"ref_40","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 2013, Shanghai, China."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Li, X., Pfister, T., Huang, X., Zhao, G., and Pietikainen, 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 2013, Shanghai, China.","DOI":"10.1109\/FG.2013.6553717"},{"key":"ref_42","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_43","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":"2018","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1109\/TAFFC.2017.2654440","article-title":"CAS(ME)2: A Database for Spontaneous Macro-Expression and Micro-Expression Spotting and Recognition","volume":"9","author":"Qu","year":"2018","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Guo, Y., Tian, Y., Gao, X., and Zhang, X. (2014, January 6\u201311). Micro-expression recognition based on local binary patterns from three orthogonal planes and nearest neighbor method. Proceedings of the International Joint Conference on Neural Networks, Beijing, China.","DOI":"10.1109\/IJCNN.2014.6889620"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s11063-013-9288-7","article-title":"Face recognition and micro-expression recognition based on discriminant tensor subspace analysis plus extreme learning machine","volume":"39","author":"Wang","year":"2014","journal-title":"Neural Process. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Huang, X., Wang, S.J., Zhao, G., and Piteikainen, M. (2015, January 7\u201313). Facial Micro-Expression Recognition Using Spatiotemporal Local Binary Pattern with Integral Projection. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCVW.2015.10"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Le Ngo, A.C., Liong, S.T., See, J., and Phan, R.C.W. (2015, January 21\u201324). Are subtle expressions too sparse to recognize?. Proceedings of the International Conference on Digital Signal Processing, DSP, Singapore.","DOI":"10.1109\/ICDSP.2015.7252080"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Le Ngo, A.C., Phan, R.C.W., and See, J. (2015). Spontaneous subtle expression recognition: Imbalanced databases and solutions. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer.","DOI":"10.1007\/978-3-319-16817-3_3"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Liong, S.T., See, J., Phan, R.C., Le Ngo, A.C., Oh, Y.H., and Wong, K.S. (2015). Subtle expression recognition using optical strain weighted features. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer.","DOI":"10.1007\/978-3-319-16631-5_47"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Lu, Z., Luo, Z., Zheng, H., Chen, J., and Li, W. (2015). A Delaunay-based temporal coding model for micro-expression recognition. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer.","DOI":"10.1007\/978-3-319-16631-5_51"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wang, Y., See, J., Phan, R.C., and Oh, Y.H. (2015). Efficient spatio-temporal local binary patterns for spontaneous facial micro-expression recognition. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0124674"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2629","DOI":"10.1007\/s00521-015-2031-8","article-title":"Gait recognition and micro-expression recognition based on maximum margin projection with tensor representation","volume":"27","author":"Ben","year":"2016","journal-title":"Neural Comput. Appl."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Chen, M., Ma, H.T., Li, J., and Wang, H. (2016, January 6\u201310). Emotion recognition using fixed length micro-expressions sequence and weighting method. Proceedings of the 2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016, Angkor Wat, Cambodia.","DOI":"10.1109\/RCAR.2016.7784067"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.image.2016.06.004","article-title":"Spontaneous subtle expression detection and recognition based on facial strain","volume":"47","author":"Liong","year":"2016","journal-title":"Signal Process. Image Commun."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Oh, Y.H., Le Ngo, A.C., Phari, R.C., See, J., and Ling, H.C. (2016, January 20\u201325). Intrinsic two-dimensional local structures for micro-expression recognition. Proceedings of the ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing, Shanghai, China.","DOI":"10.1109\/ICASSP.2016.7471997"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Talukder, B.M., Chowdhury, B., Howlader, T., and Rahman, S.M. (2016). Intelligent recognition of spontaneous expression using motion magnification of spatio-temporal data. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer.","DOI":"10.1007\/978-3-319-31863-9_9"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zheng, H., Geng, X., and Yang, Z. (2016). A relaxed K-SVD algorithm for spontaneous micro-expression recognition. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer.","DOI":"10.1007\/978-3-319-42911-3_58"},{"key":"ref_59","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_60","doi-asserted-by":"crossref","unstructured":"Liong, S.T., See, J., Wong, K., and Phan, R.C.W. (2017). Automatic micro-expression recognition from long video using a single spotted apex. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer.","DOI":"10.1007\/978-3-319-54427-4_26"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"21665","DOI":"10.1007\/s11042-016-4079-6","article-title":"Effective recognition of facial micro-expressions with video motion magnification","volume":"76","author":"Wang","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Zhang, S., Feng, B., Chen, Z., and Huang, X. (2017). Micro-expression recognition by aggregating local spatio-temporal patterns. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer.","DOI":"10.1007\/978-3-319-51811-4_52"},{"key":"ref_63","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 MM 2017\u2014Proceedings of the 2017 ACM Multimedia Conference, Mountain View, CA, USA.","DOI":"10.1145\/3123266.3123367"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.patrec.2017.07.010","article-title":"Learning effective binary descriptors for micro-expression recognition transferred by macro-information","volume":"107","author":"Ben","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Hu, C., Jiang, D., Zou, H., Zuo, X., and Shu, Y. (2018, January 20\u201324). Multi-task Micro-expression Recognition Combining Deep and Handcrafted Features. Proceedings of the International Conference on Pattern Recognition, Beijing, China.","DOI":"10.1109\/ICPR.2018.8545555"},{"key":"ref_66","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":"2018","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_67","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_68","doi-asserted-by":"crossref","unstructured":"Su, W., Wang, Y., Su, F., and Zhao, Z. (2018, January 23\u201327). Micro-expression recognition based on the spatio-temporal feature. Proceedings of the 2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018, San Diego, CA, USA.","DOI":"10.1109\/ICMEW.2018.8551494"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"3105","DOI":"10.1007\/s11042-017-4943-z","article-title":"Coupled source domain targetized with updating tag vectors for micro-expression recognition","volume":"77","author":"Zhu","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_70","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_71","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.image.2019.02.005","article-title":"OFF-ApexNet on micro-expression recognition system","volume":"74","author":"Gan","year":"2019","journal-title":"Signal Process. Image Commun."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/TAFFC.2017.2713359","article-title":"Discriminative Spatiotemporal Local Binary Pattern with Revisited Integral Projection for Spontaneous Facial Micro-Expression Recognition","volume":"10","author":"Huang","year":"2019","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1007\/s10044-018-0757-5","article-title":"Micro-expression recognition based on 3D flow convolutional neural network","volume":"22","author":"Li","year":"2019","journal-title":"Pattern Anal. Appl."},{"key":"ref_74","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 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019, Lille, France.","DOI":"10.1109\/FG.2019.8756567"},{"key":"ref_75","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 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019, Lille, France.","DOI":"10.1109\/FG.2019.8756583"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Peng, W., Hong, X., Xu, Y., and Zhao, G. (2019, January 14\u201318). A boost in revealing subtle facial expressions: A consolidated Eulerian framework. Proceedings of the 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019, Lille, France.","DOI":"10.1109\/FG.2019.8756541"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Peng, M., Shi, Y., Wang, C., Zhou, X., Bi, T., 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_78","doi-asserted-by":"crossref","unstructured":"Van Quang, N., Chun, J., and Tokuyama, T. (2019, January 14\u201318). CapsuleNet for micro-expression recognition. Proceedings of the 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019, Lille, France.","DOI":"10.1109\/FG.2019.8756544"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Xia, Z., Feng, X., Hong, X., and Zhao, G. (2018, January 7\u201310). Spontaneous facial micro-expression recognition via deep convolutional network. Proceedings of the 2018 8th International Conference on Image Processing Theory, Tools and Applications, IPTA 2018, Xi\u2019an, China.","DOI":"10.1109\/IPTA.2018.8608119"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Zhao, Y., and Xu, J. (2019). An improved micro-expression recognition method based on necessary morphological patches. Symmetry, 11.","DOI":"10.3390\/sym11040497"},{"key":"ref_81","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 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019, Lille, France.","DOI":"10.1109\/FG.2019.8756579"},{"key":"ref_82","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_83","doi-asserted-by":"crossref","unstructured":"Xie, H.X., Lo, L., Shuai, H.H., and Cheng, W.H. (2020, January 12\u201316). AU-assisted Graph Attention Convolutional Network for Micro-Expression Recognition. Proceedings of the 28th ACM International Conference on Multimedia, Seattle, WA, USA.","DOI":"10.1145\/3394171.3414012"}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/3\/2\/21\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:56:40Z","timestamp":1760162200000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/3\/2\/21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,2]]},"references-count":83,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["make3020021"],"URL":"https:\/\/doi.org\/10.3390\/make3020021","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,2]]}}}