{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T02:32:28Z","timestamp":1783823548377,"version":"3.55.0"},"reference-count":133,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T00:00:00Z","timestamp":1568937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Facial Expression Recognition (FER), as the primary processing method for non-verbal intentions, is an important and promising field of computer vision and artificial intelligence, and one of the subject areas of symmetry. This survey is a comprehensive and structured overview of recent advances in FER. We first categorise the existing FER methods into two main groups, i.e., conventional approaches and deep learning-based approaches. Methodologically, to highlight the differences and similarities, we propose a general framework of a conventional FER approach and review the possible technologies that can be employed in each component. As for deep learning-based methods, four kinds of neural network-based state-of-the-art FER approaches are presented and analysed. Besides, we introduce seventeen commonly used FER datasets and summarise four FER-related elements of datasets that may influence the choosing and processing of FER approaches. Evaluation methods and metrics are given in the later part to show how to assess FER algorithms, along with subsequent performance comparisons of different FER approaches on the benchmark datasets. At the end of the survey, we present some challenges and opportunities that need to be addressed in future.<\/jats:p>","DOI":"10.3390\/sym11101189","type":"journal-article","created":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T10:48:14Z","timestamp":1568976494000},"page":"1189","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":148,"title":["Facial Expression Recognition: A Survey"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9522-5960","authenticated-orcid":false,"given":"Yunxin","family":"Huang","sequence":"first","affiliation":[{"name":"College of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaohe","family":"Lv","sequence":"additional","affiliation":[{"name":"College of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,20]]},"reference":[{"key":"ref_1","unstructured":"Mehrabian, A., and Russell, J.A. (1974). An Approach to Environmental Psychology, The MIT Press."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/79.911197","article-title":"Emotion recognition in human-computer interaction","volume":"18","author":"Cowie","year":"2001","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bartlett, M.S., Littlewort, G., Fasel, I., and Movellan, J.R. (2003, January 16\u201322). Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction. Proceedings of the Conference on Computer Vision and Pattern Recognition Workshop, Madison, WI, USA.","DOI":"10.1109\/CVPRW.2003.10057"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1109\/TVCG.2013.42","article-title":"Understanding how adolescents with autism respond to facial expressions in virtual reality environments","volume":"19","author":"Bekele","year":"2013","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.ridd.2014.10.015","article-title":"Augmented reality-based self-facial modeling to promote the emotional expression and social skills of adolescents with autism spectrum disorders","volume":"36","author":"Chen","year":"2015","journal-title":"Res. Dev. Disabil."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Assari, M.A., and Rahmati, M. (2011, January 16\u201318). Driver drowsiness detection using face expression recognition. Proceedings of the IEEE International Conference on Signal and Image Processing Applications, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICSIPA.2011.6144162"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/MPRV.2010.46","article-title":"Facial expression analysis for predicting unsafe driving behavior","volume":"10","author":"Jabon","year":"2011","journal-title":"IEEE Perv. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1016\/j.ijhcs.2007.02.003","article-title":"Automatic prediction of frustration","volume":"65","author":"Kapoor","year":"2007","journal-title":"Int. J. Hum.-Comput. Stud."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lankes, M., Riegler, S., Weiss, A., Mirlacher, T., Pirker, M., and Tscheligi, M. (2008, January 3\u20135). Facial expressions as game input with different emotional feedback conditions. Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology, Yokohama, Japan.","DOI":"10.1145\/1501750.1501809"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jerritta, S., Murugappan, M., Nagarajan, R., and Wan, K. (2011, January 4\u20136). Physiological signals based human emotion recognition: A review. Proceedings of the IEEE 7th International Colloquium on Signal Processing and its Applications, Penang, Malaysia.","DOI":"10.1109\/CSPA.2011.5759912"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1109\/34.908962","article-title":"Recognizing action units for facial expression analysis","volume":"23","author":"Tian","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1037\/h0077714","article-title":"A circumplex model of affect","volume":"39","author":"Russell","year":"1980","journal-title":"J. Pers. Soc. Psychol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chang, W.Y., Hsu, S.H., and Chien, J.H. (2017, January 21\u201326). FATAUVA-Net: An integrated deep learning framework for facial attribute recognition, action unit detection, and valence-arousal estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.246"},{"key":"ref_14","unstructured":"Lyons, M., Akamatsu, S., Kamachi, M., and Gyoba, J. (1998, January 14\u201316). Coding facial expressions with gabor wavelets. Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition, Nara, Japan."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., and Matthews, I. (2010, January 13\u201318). The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, San Francisco, CA, USA.","DOI":"10.1109\/CVPRW.2010.5543262"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Luo, P., Loy, C.C., and Tang, X. (2014). Facial landmark detection by deep multi-task learning. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10599-4_7"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/s11263-018-1097-z","article-title":"Facial landmark detection: A literature survey","volume":"127","author":"Wu","year":"2019","journal-title":"Int. J. Comput. Vis."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ekman, P., and Friesen, W.V. (1978). Facial Action Coding System: Investigator\u2019s Guide, Consulting Psychologists Press.","DOI":"10.1037\/t27734-000"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Benitez-Quiroz, C.F., Srinivasan, R., and Martinez, A.M. (2016, January 27\u201330). Emotionet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.600"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1080\/02699939208411068","article-title":"An argument for basic emotions","volume":"6","author":"Ekman","year":"1992","journal-title":"Cogn. Emot."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"E1454","DOI":"10.1073\/pnas.1322355111","article-title":"Compound facial expressions of emotion","volume":"111","author":"Du","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1196\/annals.1280.010","article-title":"Darwin, deception, and facial expression","volume":"1000","author":"Ekman","year":"2003","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/0031-3203(92)90007-6","article-title":"Automatic recognition and analysis of human faces and facial expressions: A survey","volume":"25","author":"Samal","year":"1992","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/S0031-3203(02)00052-3","article-title":"Automatic facial expression analysis: A survey","volume":"36","author":"Fasel","year":"2003","journal-title":"Pattern Recognit."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1016\/j.imavis.2012.06.005","article-title":"Static and dynamic 3D facial expression recognition: A comprehensive survey","volume":"30","author":"Sandbach","year":"2012","journal-title":"Image Vis. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5577","DOI":"10.1007\/s11042-014-1869-6","article-title":"A survey on facial expression recognition in 3D video sequences","volume":"74","author":"Danelakis","year":"2015","journal-title":"Multimed. Tools Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"19301","DOI":"10.1007\/s11042-017-5317-2","article-title":"A survey: Facial micro-expression recognition","volume":"77","author":"Takalkar","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1016\/j.procs.2015.08.011","article-title":"Facial expression recognition: A survey","volume":"58","author":"Kumari","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1109\/TSMCC.2011.2118750","article-title":"Local binary patterns and its application to facial image analysis: A survey","volume":"41","author":"Huang","year":"2011","journal-title":"IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.)"},{"key":"ref_30","first-page":"25","article-title":"Facial Expression Analysis under Partial Occlusion: A Survey","volume":"51","author":"Zhang","year":"2018","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1049\/iet-bmt.2014.0104","article-title":"Survey on real-time facial expression recognition techniques","volume":"5","author":"Deshmukh","year":"2016","journal-title":"IET Biometr."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Goyal, S.J., Upadhyay, A.K., Jadon, R., and Goyal, R. (2018). Real-Life Facial Expression Recognition Systems: A Review. Smart Computing and Informatics, Springer.","DOI":"10.1007\/978-981-10-5544-7_31"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6195","DOI":"10.1016\/j.ijleo.2016.04.015","article-title":"Facial expression recognition on real world face images using intelligent techniques: A survey","volume":"127","author":"Khan","year":"2016","journal-title":"Opt.-Int. J. Light Electron Opt."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","article-title":"Robust real-time face detection","volume":"57","author":"Viola","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1109\/34.1000242","article-title":"Face detection in color images","volume":"24","author":"Hsu","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","unstructured":"Shan, S., Gao, W., Cao, B., and Zhao, D. (2003, January 17). Illumination normalization for robust face recognition against varying lighting conditions. Proceedings of the 2003 IEEE International SOI Conference, Nice, France."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1109\/TSMCB.2005.857353","article-title":"Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain","volume":"36","author":"Chen","year":"2006","journal-title":"IEEE Trans. Syst. Man Cybern. Part B (Cybern.)"},{"key":"ref_38","unstructured":"Du, S., and Ward, R. (2005, January 14). Wavelet-based illumination normalization for face recognition. Proceedings of the IEEE International Conference on Image Processing 2005, Genova, Italy."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1049\/el.2011.3421","article-title":"Image enhancement using background brightness preserving histogram equalisation","volume":"48","author":"Tan","year":"2012","journal-title":"Electron. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhang, S., Li, L., and Zhao, Z. (2012, January 21\u201325). Facial expression recognition based on Gabor wavelets and sparse representation. Proceedings of the IEEE 11th International Conference on Signal Processing, Beijing, China.","DOI":"10.1109\/ICoSP.2012.6491706"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1016\/j.patrec.2005.07.026","article-title":"Evolutionary feature synthesis for facial expression recognition","volume":"27","author":"Yu","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Mattela, G., and Gupta, S.K. (2018, January 22\u201323). Facial Expression Recognition Using Gabor-Mean-DWT Feature Extraction Technique. Proceedings of the 5th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India.","DOI":"10.1109\/SPIN.2018.8474206"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ahonen, T., Hadid, A., and Pietik\u00e4inen, M. (2004). Face recognition with local binary patterns. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-540-24670-1_36"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1134\/S1054661807040190","article-title":"Facial expression recognition based on local binary patterns","volume":"17","author":"Feng","year":"2007","journal-title":"Pattern Recognit. Image Anal."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1657","DOI":"10.1109\/TIP.2010.2044957","article-title":"A completed modeling of local binary pattern operator for texture classification","volume":"19","author":"Guo","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"784","DOI":"10.4218\/etrij.10.1510.0132","article-title":"Robust facial expression recognition based on local directional pattern","volume":"32","author":"Jabid","year":"2010","journal-title":"ETRI J."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wang, Z., and Ying, Z. (2012, January 29\u201331). Facial expression recognition based on local phase quantization and sparse representation. Proceedings of the 2012 8th International Conference on Natural Computation, Chongqing, China.","DOI":"10.1109\/ICNC.2012.6234551"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.sigpro.2015.04.007","article-title":"Facial expression recognition based on improved local binary pattern and class-regularized locality preserving projection","volume":"117","author":"Chao","year":"2015","journal-title":"Signal Process."},{"key":"ref_49","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_50","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1109\/34.927467","article-title":"Active appearance models","volume":"6","author":"Cootes","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Cristinacce, D., Cootes, T.F., and Scott, I.M. (2004, January 7\u20139). A multi-stage approach to facial feature detection. Proceedings of the British Machine Vision Conference (BMVC), Kingston, UK.","DOI":"10.5244\/C.18.30"},{"key":"ref_52","unstructured":"Saatci, Y., and Town, C. (2006, January 10\u201312). Cascaded classification of gender and facial expression using active appearance models. Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition (FGR06), Southampton, UK."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/0004-3702(81)90024-2","article-title":"Determining optical flow","volume":"17","author":"Horn","year":"1981","journal-title":"Artif. Intell."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1109\/34.506414","article-title":"Recognizing human facial expressions from long image sequences using optical flow","volume":"18","author":"Yacoob","year":"1996","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_55","unstructured":"Cohn, J.F., Zlochower, A.J., Lien, J.J., and Kanade, T. (1998, January 14\u201316). Feature-point tracking by optical flow discriminates subtle differences in facial expression. Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition, Nara, Japan."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1016\/j.neucom.2010.07.017","article-title":"Differential optical flow applied to automatic facial expression recognition","volume":"74","author":"Ruiz","year":"2011","journal-title":"Neurocomputing"},{"key":"ref_57","unstructured":"Viola, P., and Jones, M. (2001, January 8\u201314). Rapid object detection using a boosted cascade of simple features. Proceedings of the CVPR, Kauai, HI, USA."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.patrec.2008.03.014","article-title":"Boosting encoded dynamic features for facial expression recognition","volume":"30","author":"Yang","year":"2009","journal-title":"Pattern Recognit. Lett."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/TCSVT.2012.2203210","article-title":"A deformable 3-D facial expression model for dynamic human emotional state recognition","volume":"23","author":"Tie","year":"2013","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_60","first-page":"028","article-title":"A Feature Point Tracking Method Based on The Combination of SIFT Algorithm and KLT Matching Algorithm","volume":"7","author":"Liu","year":"2011","journal-title":"J. Astronaut."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Xu, H., Wang, Y., Cheng, L., Wang, Y., and Ma, X. (2018, January 22\u201326). Exploring a High-quality Outlying Feature Value Set for Noise-Resilient Outlier Detection in Categorical Data. Proceedings of the Conference on Information and Knowledge Management (CIKM), Turin, Italy.","DOI":"10.1145\/3269206.3271721"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Sohail, A.S.M., and Bhattacharya, P. (2007). Classification of facial expressions using k-nearest neighbor classifier. Proceedings of the International Conference on Computer Vision\/Computer Graphics Collaboration Techniques and Applications, Springer.","DOI":"10.1007\/978-3-540-71457-6_51"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"3132","DOI":"10.1016\/j.ijleo.2015.07.073","article-title":"New facial expression recognition based on FSVM and KNN","volume":"126","author":"Wang","year":"2015","journal-title":"Optik"},{"key":"ref_64","unstructured":"Valstar, M., Patras, I., and Pantic, M. (2004, January 22). Facial action unit recognition using temporal templates. Proceedings of the 13th IEEE International Workshop on Robot and Human Interactive Communication, Kurashiki, Okayama, Japan."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Michel, P., and El Kaliouby, R. (2003, January 5\u20137). Real time facial expression recognition in video using support vector machines. Proceedings of the 5th International Conference on Multimodal Interfaces, Vancouver, BC, Canada.","DOI":"10.1145\/958432.958479"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"4389","DOI":"10.1007\/s00500-017-2634-3","article-title":"Facial expression recognition using a combination of multiple facial features and support vector machine","volume":"22","author":"Tsai","year":"2018","journal-title":"Soft Comput."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"6663","DOI":"10.1007\/s11042-015-2598-1","article-title":"Effective semantic features for facial expressions recognition using SVM","volume":"75","author":"Hsieh","year":"2016","journal-title":"Multimed. Tools Appl."},{"key":"ref_68","first-page":"670","article-title":"Empirical Evaluation of SVM for Facial Expression Recognition","volume":"9","author":"Saeed","year":"2018","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_69","unstructured":"Shah, J.H., Sharif, M., Yasmin, M., and Fernandes, S.L. (2017). Facial expressions classification and false label reduction using LDA and threefold SVM. Pattern Recognit. Lett."},{"key":"ref_70","unstructured":"Wang, Y., Ai, H., Wu, B., and Huang, C. (2004, January 26). Real time facial expression recognition with adaboost. Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, UK."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"104","DOI":"10.2197\/ipsjtcva.7.104","article-title":"Facial expression recognition and analysis: A comparison study of feature descriptors","volume":"7","author":"Liew","year":"2015","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"ref_72","unstructured":"Gudipati, V.K., Barman, O.R., Gaffoor, M., and Abuzneid, A. (2016, January 14\u201315). Efficient facial expression recognition using adaboost and haar cascade classifiers. Proceedings of the Annual Connecticut Conference on Industrial Electronics, Technology & Automation (CT-IETA), Bridgeport, CT, USA."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Zhang, S., Hu, B., Li, T., and Zheng, X. (2018). A Study on Emotion Recognition Based on Hierarchical Adaboost Multi-class Algorithm. Proceedings of the International Conference on Algorithms and Architectures for Parallel Processing, Springer.","DOI":"10.1007\/978-3-030-05054-2_8"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1016\/S0031-3203(99)00179-X","article-title":"Bayesian face recognition","volume":"33","author":"Moghaddam","year":"2000","journal-title":"Pattern Recognit."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1109\/TMM.2016.2629282","article-title":"Hierarchical Bayesian theme models for multipose facial expression recognition","volume":"19","author":"Mao","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Surace, L., Patacchiola, M., Battini S\u00f6nmez, E., Spataro, W., and Cangelosi, A. (2017, January 13\u201317). Emotion recognition in the wild using deep neural networks and Bayesian classifiers. Proceedings of the 19th ACM International Conference on Multimodal Interaction, Glasgow, UK.","DOI":"10.1145\/3136755.3143015"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Huang, M.W., Wang, Z.W., and Ying, Z.L. (2010, January 16\u201318). A new method for facial expression recognition based on sparse representation plus LBP. Proceedings of the 3rd International Congress on Image and Signal Processing, Yantai, China.","DOI":"10.1109\/CISP.2010.5647898"},{"key":"ref_78","first-page":"440","article-title":"Facial expression recognition using sparse representation","volume":"11","author":"Zhang","year":"2012","journal-title":"WSEAS Trans. Syst."},{"key":"ref_79","first-page":"772","article-title":"On the application of various probabilistic neural networks in solving different pattern classification problems","volume":"4","author":"Ramakrishnan","year":"2008","journal-title":"World Appl. Sci. J."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"2163","DOI":"10.1109\/TNNLS.2014.2376703","article-title":"Application of reinforcement learning algorithms for the adaptive computation of the smoothing parameter for probabilistic neural network","volume":"26","author":"Kusy","year":"2015","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.3923\/jas.2010.1572.1579","article-title":"Application of improved AAM and probabilistic neural network to facial expression recognition","volume":"10","author":"Neggaz","year":"2010","journal-title":"J. Appl. Sci. (Faisalabad)"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Fazli, S., Afrouzian, R., and Seyedarabi, H. (2009, January 20\u201322). High-performance facial expression recognition using Gabor filter and probabilistic neural network. Proceedings of the IEEE International Conference on Intelligent Computing and Intelligent Systems, Shanghai, China.","DOI":"10.1109\/ICICISYS.2009.5357716"},{"key":"ref_83","first-page":"143","article-title":"Deep structured learning for facial expression intensity estimation","volume":"259","author":"Walecki","year":"2017","journal-title":"Image Vis. Comput"},{"key":"ref_84","unstructured":"Breuer, R., and Kimmel, R. (2017). A deep learning perspective on the origin of facial expressions. arXiv."},{"key":"ref_85","unstructured":"Liu, M., Li, S., Shan, S., Wang, R., and Chen, X. (2014). Deeply learning deformable facial action parts model for dynamic expression analysis. Asian Conference on Computer Vision, Springer."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Jung, H., Lee, S., Yim, J., Park, S., and Kim, J. (2015, January 7\u201313). Joint fine-tuning in deep neural networks for facial expression recognition. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.341"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Mollahosseini, A., Chan, D., and Mahoor, M.H. (2016, January 7\u201310). Going deeper in facial expression recognition using deep neural networks. Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA.","DOI":"10.1109\/WACV.2016.7477450"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"2439","DOI":"10.1109\/TIP.2018.2886767","article-title":"Occlusion Aware Facial Expression Recognition Using CNN with Attention Mechanism","volume":"28","author":"Li","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_90","unstructured":"Hinton, G.E., and Sejnowski, T.J. (1986). Learning and relearning in Boltzmann machines. Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, MIT Press."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Liu, P., Han, S., Meng, Z., and Tong, Y. (2014, January 24\u201327). Facial expression recognition via a boosted deep belief network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.233"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1080\/02564602.2015.1017542","article-title":"Facial expression recognition via deep learning","volume":"32","author":"Zhao","year":"2015","journal-title":"IETE Tech. Rev."},{"key":"ref_93","unstructured":"He, J., Cai, J., Fang, L., He, Z., and Amp, D.E. (2016). Facial expression recognition based on LBP\/VAR and DBN model. Appl. Res. Comput."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"4525","DOI":"10.1109\/ACCESS.2017.2676238","article-title":"Facial expression recognition utilizing local direction-based robust features and deep belief network","volume":"5","author":"Uddin","year":"2017","journal-title":"IEEE Access"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.imavis.2012.03.001","article-title":"LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework","volume":"31","author":"Kaiser","year":"2013","journal-title":"Image Vis. Comput."},{"key":"ref_96","unstructured":"Kim, D.H., Baddar, W., Jang, J., and Ro, Y.M. (2017). Multi-objective based spatio-temporal feature representation learning robust to expression intensity variations for facial expression recognition. IEEE Trans. Affect. Comput."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Hasani, B., and Mahoor, M.H. (2017, January 21\u201326). Facial expression recognition using enhanced deep 3D convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.282"},{"key":"ref_98","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Conference on Neural Information Processing Systems (NIPS), Montreal, QC, Canad."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Lai, Y.H., and Lai, S.H. (2018, January 15\u201319). Emotion-preserving representation learning via generative adversarial network for multi-view facial expression recognition. Proceedings of the 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi\u2019an, China.","DOI":"10.1109\/FG.2018.00046"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Zhang, F., Zhang, T., Mao, Q., and Xu, C. (2018, January 19\u201321). Joint pose and expression modeling for facial expression recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00354"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Yang, H., Zhang, Z., and Yin, L. (2018, January 15\u201319). Identity-adaptive facial expression recognition through expression regeneration using conditional generative adversarial networks. Proceedings of the 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi\u2019an, China.","DOI":"10.1109\/FG.2018.00050"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Chen, J., Konrad, J., and Ishwar, P. (2018, January 18\u201322). Vgan-based image representation learning for privacy-preserving facial expression recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00207"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Yang, H., Ciftci, U., and Yin, L. (2018, January 18). Facial expression recognition by de-expression residue learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00231"},{"key":"ref_104","first-page":"630","article-title":"The Karolinska directed emotional faces (KDEF)","volume":"91","author":"Lundqvist","year":"1998","journal-title":"CD ROM Dep. Clin. Neurosci. Psychol. Sect. Karolinska Inst."},{"key":"ref_105","unstructured":"Pantic, M., Valstar, M., Rademaker, R., and Maat, L. (2005, January 6). Web-based database for facial expression analysis. Proceedings of the IEEE International Conference on Multimedia and Expo, Amsterdam, The Netherlands."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Kaulard, K., Cunningham, D.W., B\u00fclthoff, H.H., and Wallraven, C. (2012). The MPI facial expression database\u2014A validated database of emotional and conversational facial expressions. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0032321"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Lucey, P., Cohn, J.F., Prkachin, K.M., Solomon, P.E., and Matthews, I. (2011, January 21\u201325). Painful data: The UNBC-McMaster shoulder pain expression archive database. Proceedings of the Face and Gesture 2011, Santa Barbara, CA, USA.","DOI":"10.1109\/FG.2011.5771462"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1109\/T-AFFC.2013.4","article-title":"Disfa: A spontaneous facial action intensity database","volume":"4","author":"Mavadati","year":"2013","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_109","unstructured":"Yin, L., Wei, X., Sun, Y., Wang, J., and Rosato, M.J. (2006, January 10\u201312). A 3D facial expression database for facial behavior research. Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition (FGR06), Southampton, UK."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1016\/j.imavis.2014.06.002","article-title":"Bp4d-spontaneous: A high-resolution spontaneous 3d dynamic facial expression database","volume":"32","author":"Zhang","year":"2014","journal-title":"Image Vis. Comput."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1109\/TMM.2010.2060716","article-title":"A natural visible and infrared facial expression database for expression recognition and emotion inference","volume":"12","author":"Wang","year":"2010","journal-title":"IEEE Trans. Multimed."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1016\/j.imavis.2009.08.002","article-title":"Multi-PIE","volume":"28","author":"Gross","year":"2010","journal-title":"Image Vis. Comput."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1016\/j.imavis.2011.07.002","article-title":"Facial expression recognition from near-infrared videos","volume":"29","author":"Zhao","year":"2011","journal-title":"Image Vis. Comput."},{"key":"ref_114","unstructured":"Carrier, P.L., Courville, A., Goodfellow, I.J., Mirza, M., and Bengio, Y. (2013). FER-2013 Face Database, Universit de Montral."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"966","DOI":"10.1109\/TSMCB.2012.2200675","article-title":"Meta-analysis of the first facial expression recognition challenge","volume":"42","author":"Valstar","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part B (Cybern.)"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/MMUL.2012.26","article-title":"Collecting large, richly annotated facial-expression databases from movies","volume":"19","author":"Dhall","year":"2012","journal-title":"IEEE Multimed."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Dhall, A., Goecke, R., Lucey, S., and Gedeon, T. (2011, January 6\u201313). Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark. Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, Spain.","DOI":"10.1109\/ICCVW.2011.6130508"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Li, S., Deng, W., and Du, J. (2017, January 22\u201325). Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.277"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1109\/TIP.2018.2868382","article-title":"Reliable crowdsourcing and deep locality-preserving learning for unconstrained facial expression recognition","volume":"28","author":"Li","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Li, S., and Deng, W. (2018). Blended Emotion in-the-Wild: Multi-label Facial Expression Recognition Using Crowdsourced Annotations and Deep Locality Feature Learning. Int. J. Comput. Vis., 1\u201323.","DOI":"10.1007\/s11263-018-1131-1"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"2106","DOI":"10.1109\/TPAMI.2009.42","article-title":"Toward practical smile detection","volume":"31","author":"Whitehill","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Jeon, J., Park, J.C., Jo, Y., Nam, C., Bae, K.H., Hwang, Y., and Kim, D.S. (2016, January 4\u20136). A Real-time Facial Expression Recognizer using Deep Neural Network. Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication, Danang, Vietnam.","DOI":"10.1145\/2857546.2857642"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","article-title":"A review on evaluation metrics for data classification evaluations","volume":"5","author":"Hossin","year":"2015","journal-title":"Int. J. Data Min. Knowl. Manag. Process"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Li, S., and Deng, W. (2018, January 20\u201324). Deep Emotion Transfer Network for Cross-database Facial Expression Recognition. Proceedings of the 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8545284"},{"key":"ref_125","unstructured":"Li, S., and Deng, W. (2019). A Deeper Look at Facial Expression Dataset Bias. arXiv."},{"key":"ref_126","unstructured":"Mollahosseini, A., Hasani, B., and Mahoor, M.H. (2017). AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild. IEEE Trans. Affect. Comput."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"1467","DOI":"10.1007\/s11235-011-9624-z","article-title":"Speech emotion recognition approaches in human computer interaction","volume":"52","author":"Ramakrishnan","year":"2013","journal-title":"Telecommun. Syst."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Chang, J., and Scherer, S. (2017, January 5\u20139). Learning representations of emotional speech with deep convolutional generative adversarial networks. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952656"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Chao, L., Tao, J., Yang, M., Li, Y., and Wen, Z. (2014, January 7). Multi-scale temporal modeling for dimensional emotion recognition in video. Proceedings of the 4th International Workshop on Audio\/Visual Emotion Challenge, Orlando, FL, USA.","DOI":"10.1145\/2661806.2661811"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Chen, S., and Jin, Q. (2015, January 26). Multi-modal dimensional emotion recognition using recurrent neural networks. Proceedings of the 5th International Workshop on Audio\/Visual Emotion Challenge, Brisbane, Australia.","DOI":"10.1145\/2808196.2811638"},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"He, L., Jiang, D., Yang, L., Pei, E., Wu, P., and Sahli, H. (2015, January 26). Multimodal affective dimension prediction using deep bidirectional long short-term memory recurrent neural networks. Proceedings of the 5th International Workshop on Audio\/Visual Emotion Challenge, Brisbane, Australia.","DOI":"10.1145\/2808196.2811641"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1109\/TKDE.2005.32","article-title":"Preserving privacy by de-identifying face images","volume":"17","author":"Newton","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_133","first-page":"326","article-title":"Efficient privacy-preserving facial expression classification","volume":"14","author":"Rahulamathavan","year":"2017","journal-title":"IEEE Trans. Dependable Secur. Comput."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/10\/1189\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:22:27Z","timestamp":1760188947000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/10\/1189"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,20]]},"references-count":133,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["sym11101189"],"URL":"https:\/\/doi.org\/10.3390\/sym11101189","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,20]]}}}