{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T05:24:10Z","timestamp":1766121850686,"version":"3.48.0"},"reference-count":74,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T00:00:00Z","timestamp":1765929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Key Laboratory of Aerodynamics","award":["SKLA-JSSX-2024-KFKT-05"],"award-info":[{"award-number":["SKLA-JSSX-2024-KFKT-05"]}]},{"DOI":"10.13039\/501100012401","name":"Beijing Science and Technology  Plan Project","doi-asserted-by":"crossref","award":["Z231100005923035"],"award-info":[{"award-number":["Z231100005923035"]}],"id":[{"id":"10.13039\/501100012401","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>With the rapid development of artificial intelligence technology, facial expression recognition (FER) has gained increasingly widespread applications in digital human generation, humanoid robotics, mental health, and human\u2013computer dialogue. Typical FER algorithms based on machine learning have been widely studied over the past few decades, which motivated our survey. In this study, we have surveyed the state of the art in FER across two categories: traditional machine learning-based (ML-based) and deep learning-based (DL-based) approaches. Each category is analyzed based on six subcategories. Then, twelve methods, including four ML-based models and eight DL-based models, are compared to evaluate FER performance across four datasets. The experimental results show that in validation sets, the average accuracy of HOG-SVM is 50.12%, which is the best performance for the four ML-based methods; in contrast, Poster has an average accuracy of 75.98%, which is the best result obtained among the eight DL-based methods. The most difficult expression to recognize is contempt, with recognition accuracies of 10.00% and 40.06% for ML-based and DL-based methods, respectively. The accuracy of the ML-based method for identifying neutral expression is the highest at 35.25%; the DL-based method has the highest accuracy in identifying surprise at 69.56%. From the theoretical analysis and comparative experimental results of existing methods, we can see that FER faces challenges, including inaccurate recognition in complex environments and unbalanced data categories, highlighting several future research directions, especially those involving the latest applications of digital humans and large language models.<\/jats:p>","DOI":"10.3390\/a18120800","type":"journal-article","created":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T10:41:23Z","timestamp":1765968083000},"page":"800","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Analysis and Comparison of Machine Learning-Based Facial Expression Recognition Algorithms"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3741-6660","authenticated-orcid":false,"given":"Yuelong","family":"Li","sequence":"first","affiliation":[{"name":"College of Science, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Zhanyi","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Science, Beijing Forestry University, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3233-6585","authenticated-orcid":false,"given":"Quandong","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Science, Beijing Forestry University, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1102-6225","authenticated-orcid":false,"given":"Hongjun","family":"Li","sequence":"additional","affiliation":[{"name":"College of Science, Beijing Forestry University, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1354","DOI":"10.1108\/EJM-02-2011-0090","article-title":"Recognising Emotional Expressions of Complaining Customers: A Cross-Cultural Study","volume":"48","author":"Tombs","year":"2014","journal-title":"Eur. J. Mark."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1186\/s13229-022-00520-7","article-title":"Facial Expression Recognition Is Linked to Clinical and Neurofunctional Differences in Autism","volume":"13","author":"Moessnang","year":"2022","journal-title":"Mol. Autism"},{"key":"ref_3","first-page":"21","article-title":"Facial Expression Recognition Based on Local Binary Patterns and Local Fisher Discriminant Analysis","volume":"8","author":"Zhang","year":"2012","journal-title":"WSEAS Trans. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1109\/TAFFC.2020.2981446","article-title":"Deep Facial Expression Recognition: A Survey","volume":"13","author":"Li","year":"2022","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hasani, B., and Mahoor, M.H. (June, January 30). Spatio-Temporal Facial Expression Recognition Using Convolutional Neural Networks and Conditional Random Fields. Proceedings of the 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), Washington, DC, USA.","DOI":"10.1109\/FG.2017.99"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Khomidov, M., and Lee, J.-H. (2024). The Novel EfficientNet Architecture-Based System and Algorithm to Predict Complex Human Emotions. Algorithms, 17.","DOI":"10.3390\/a17070285"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yang, H., Ciftci, U., and Yin, L. (2018, January 18\u201322). Facial Expression Recognition by De-Expression Residue Learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00231"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1037\/h0030377","article-title":"Constants Across Cultures in the Face and Emotion","volume":"17","author":"Ekman","year":"1971","journal-title":"J. Pers. Soc. Psychol."},{"key":"ref_9","unstructured":"Kanade, T., Cohn, J.F., and Tian, Y. (2000, January 28\u201330). Comprehensive Database for Facial Expression Analysis. Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1016\/j.imavis.2008.08.005","article-title":"Facial Expression Recognition Based on Local Binary Patterns: A Comprehensive Study","volume":"27","author":"Shan","year":"2009","journal-title":"Image Vis. Comput."},{"key":"ref_11","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 (CVPRW), San Francisco, CA, USA.","DOI":"10.1109\/CVPRW.2010.5543262"},{"key":"ref_12","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_13","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_14","doi-asserted-by":"crossref","first-page":"443","DOI":"10.31887\/DCNS.2015.17.4\/sdu","article-title":"Compound Facial Expressions of Emotion: From Basic Research to Clinical Applications","volume":"17","author":"Du","year":"2015","journal-title":"Dialogues Clin. Neurosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7241","DOI":"10.1073\/pnas.1200155109","article-title":"Facial Expressions of Emotion Are Not Culturally Universal","volume":"109","author":"Jack","year":"2012","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yang, Q., He, Y., Chen, H., Wu, Y., and Rao, Z. (2025). A Novel Lightweight Facial Expression Recognition Network Based on Deep Shallow Network Fusion and Attention Mechanism. Algorithms, 18.","DOI":"10.3390\/a18080473"},{"key":"ref_17","first-page":"2399","article-title":"Facial Emotion Recognition Methods, Datasets and Technologies: A Literature Survey","volume":"80","author":"Naga","year":"2023","journal-title":"Mater. Today Proc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e13670","DOI":"10.1111\/exsy.13670","article-title":"Facial Emotion Recognition: A Comprehensive Review","volume":"41","author":"Kaur","year":"2024","journal-title":"Expert Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kopalidis, T., Solachidis, V., Vretos, N., and Daras, P. (2024). Advances in Facial Expression Recognition: A Survey of Methods, Benchmarks, Models, and Datasets. Information, 15.","DOI":"10.3390\/info15030135"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e2024","DOI":"10.7717\/peerj-cs.2024","article-title":"Facial Expression Recognition (FER) Survey: A Vision, Architectural Elements, and Future Directions","volume":"10","author":"Ullah","year":"2024","journal-title":"PeerJ Comput. Sci."},{"key":"ref_21","first-page":"41","article-title":"Comprehensive Review and Analysis on Facial Emotion Recognition: Performance Insights into Deep and Traditional Learning with Current Updates and Challenges","volume":"82","author":"Rehman","year":"2025","journal-title":"Comput. Mater. Contin."},{"key":"ref_22","first-page":"2169","article-title":"CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features","volume":"79","author":"Haq","year":"2024","journal-title":"Comput. Mater. Contin."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","article-title":"Joint Face Detection and Alignment Using Multi-Task Cascaded Convolutional Networks","volume":"23","author":"Zhang","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image Quality Assessment: From Error Visibility to Structural Similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1007\/BFb0054760","article-title":"Active Appearance Models","volume":"1407","author":"Burkhardt","year":"1998","journal-title":"Computer Vision\u2014ECCV 1998"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/S0734-189X(87)80186-X","article-title":"Adaptive Histogram Equalization and Its Variations","volume":"39","author":"Pizer","year":"1987","journal-title":"Comput. Vis. Graph. Image Process."},{"key":"ref_27","unstructured":"Tomasi, C., and Manduchi, R. (1998, January 4\u20137). Bilateral Filtering for Gray and Color Images. Proceedings of the Sixth International Conference on Computer Vision (ICCV), Bombay, India."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1016\/j.engappai.2007.11.010","article-title":"Recognition of Facial Expressions Using Gabor Wavelets and Learning Vector Quantization","volume":"21","author":"Bashyal","year":"2008","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3895","DOI":"10.1109\/TMM.2025.3535361","article-title":"Delving Into Quaternion Wavelet Transformer for Facial Expression Recognition in the Wild","volume":"27","author":"Zhou","year":"2025","journal-title":"IEEE Trans. Multimed."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"8828245","DOI":"10.1155\/2021\/8828245","article-title":"Facial Expression Recognition with LBP and ORB Features","volume":"2021","author":"Niu","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1186\/s40064-015-1427-3","article-title":"Facial Expression Recognition and Histograms of Oriented Gradients: A Comprehensive Study","volume":"4","author":"Leo","year":"2015","journal-title":"SpringerPlus"},{"key":"ref_32","first-page":"68","article-title":"Facial Expression Recognition Based on Features Derived From the Distinct LBP and GLCM","volume":"6","author":"Kiran","year":"2014","journal-title":"Int. J. Image Graph. Signal Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1142\/S0218001499000495","article-title":"Feature-Based Facial Expression Recognition: Sensitivity Analysis and Experiments With a Multilayer Perceptron","volume":"13","author":"Zhang","year":"1999","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Moeslund, T.B., Hilton, A., Kr\u00fcger, V., and Sigal, L. (2011). Facial Expression Analysis. Visual Analysis of Humans: Looking at People, Springer.","DOI":"10.1007\/978-0-85729-997-0"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, J., and Ying, Z. (2012, January 8\u201310). Facial Expression Recognition Based on Rotation Invariant Local Phase Quantization and Sparse Representation. Proceedings of the Second International Conference on Instrumentation, Measurement, Computer, Communication and Control (IMCCC 2012), Harbin, China.","DOI":"10.1109\/IMCCC.2012.309"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1049\/iet-ipr.2013.0792","article-title":"Local Gradient-Based Illumination Invariant Face Recognition Using Local Phase Quantisation and Multi-Resolution Local Binary Pattern Fusion","volume":"9","author":"Nikan","year":"2015","journal-title":"IET Image Process."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lowe, D.G. (1999, January 20\u201327). Object Recognition from Local Scale-Invariant Features. Proceedings of the Seventh IEEE International Conference on Computer Vision (ICCV), Kerkyra, Greece.","DOI":"10.1109\/ICCV.1999.790410"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1007\/s00371-011-0611-x","article-title":"3D Facial Expression Recognition Using SIFT Descriptors of Automatically Detected Keypoints","volume":"27","author":"Berretti","year":"2011","journal-title":"Vis. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"9267","DOI":"10.1007\/s00521-019-04437-w","article-title":"An Improved SIFT Algorithm for Robust Emotion Recognition under Various Face Poses and Illuminations","volume":"32","author":"Shi","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Luo, Y., Wu, J., Zhang, Z., Zhao, H., and Shu, Z. (2023, January 10\u201312). Design of Facial Expression Recognition Algorithm Based on CNN Model. Proceedings of the 3rd IEEE International Conference on Power, Electronics and Computer Applications (ICPECA), Shenyang, China.","DOI":"10.1109\/ICPECA56706.2023.10075779"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Shehada, D., Turky, A., Rabie, T., and Hussain, A. (2024, January 13\u201314). Enhanced Lightweight Facial Emotion Recognition Systems for Visually Impaired People. Proceedings of the 12th IEEE Conference on Systems, Process & Control (ICSPC), Malacca, Malaysia.","DOI":"10.1109\/ICSPC63060.2024.10862684"},{"key":"ref_43","first-page":"45","article-title":"Facial Expression Based Sentimental Analysis Using CNN","volume":"13","author":"Adiga","year":"2024","journal-title":"Int. J. Innov. Technol. Explor. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"257","DOI":"10.30871\/jaic.v8i2.8329","article-title":"Facial Expression Recognition Using Convolutional Neural Networks with Transfer Learning Resnet-50","volume":"8","author":"Istiqomah","year":"2024","journal-title":"J. Appl. Inform. Comput."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"93","DOI":"10.54254\/2755-2721\/21\/20231122","article-title":"Comparison and Analysis of Deep Neural Networks in Facial Expression Recognition","volume":"21","author":"Li","year":"2023","journal-title":"Appl. Comput. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/LSP.2024.3521321","article-title":"ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition","volume":"32","author":"Roy","year":"2024","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"6153","DOI":"10.1038\/s41598-025-90440-2","article-title":"A Fine-Grained Human Facial Key Feature Extraction and Fusion Method for Emotion Recognition","volume":"15","author":"Li","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Liu, S., Xu, Y., Wan, T., and Kui, X. (2023, January 4\u201310). A Dual-Branch Adaptive Distribution Fusion Framework for Real-World Facial Expression Recognition. Proceedings of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece.","DOI":"10.1109\/ICASSP49357.2023.10097033"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5235","DOI":"10.1007\/s00521-024-10938-0","article-title":"A Novel Deep Learning Approach for Facial Emotion Recognition: Application to Detecting Emotional Responses in Elderly Individuals With Alzheimer\u2019s Disease","volume":"37","author":"Bohi","year":"2025","journal-title":"Neural Comput. Appl."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Boudouri, Y.E., and Bohi, A. (2023, January 27\u201329). EmoNeXt: An Adapted ConvNeXt for Facial Emotion Recognition. Proceedings of the 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), Poitiers, France.","DOI":"10.1109\/MMSP59012.2023.10337732"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5431","DOI":"10.1109\/TCSVT.2025.3527010","article-title":"Co-Dance with Ambiguity: An Ambiguity-Aware Facial Expression Recognition Framework for More Robustness","volume":"35","author":"Cao","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"79327","DOI":"10.1109\/ACCESS.2024.3407108","article-title":"PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition","volume":"12","author":"Ngwe","year":"2024","journal-title":"IEEE Access"},{"key":"ref_53","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention Is All You Need. Proceedings of the 31st Conference on Neural Information Processing Systems (NeurIPS), Long Beach, CA, USA."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Raman, D.R., Kumar, P.M., Sreenivas, T.V., and Manjunath, B.S. (2024, January 28\u201329). Multi-Modal Facial Expression Recognition Through a Hierarchical Cross-Attention Graph Convolutional Network. Proceedings of the 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS), Chikkaballapur, India.","DOI":"10.1109\/ICKECS61492.2024.10616566"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"110110","DOI":"10.1016\/j.compeleceng.2025.110110","article-title":"High Dynamic Range Preprocessing, Parallel Attention Transformer and CoExpression Analysis for Facial Expression Recognition","volume":"123","author":"Zhou","year":"2025","journal-title":"Comput. Electr. Eng."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, Z., Li, Y., and Wang, Z. (2024, January 21\u201325). Open-Set Video-Based Facial Expression Recognition With Human Expression-Sensitive Prompting. Proceedings of the 32nd ACM International Conference on Multimedia, Melbourne, VIC, Australia.","DOI":"10.1145\/3664647.3681583"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"110951","DOI":"10.1016\/j.patcog.2024.110951","article-title":"POSTER++: A Simpler and Stronger Facial Expression Recognition Network","volume":"157","author":"Mao","year":"2025","journal-title":"Pattern Recognit."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zheng, C., Mendieta, M., and Chen, C. (2023, January 2\u20136). POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCVW60793.2023.00339"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1007\/s00138-022-01288-9","article-title":"FERGCN: Facial Expression Recognition Based on Graph Convolution Network","volume":"33","author":"Liao","year":"2022","journal-title":"Mach. Vis. Appl."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Liu, D., Zhang, H., and Zhou, P. (2021, January 10\u201315). Video-Based Facial Expression Recognition Using Graph Convolutional Networks. Proceedings of the 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9413094"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Antoniadis, P., Filntisis, P.P., and Maragos, P. (2021, January 15\u201319). Exploiting Emotional Dependencies With Graph Convolutional Networks for Facial Expression Recognition. Proceedings of the 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021), Jodhpur, India.","DOI":"10.1109\/FG52635.2021.9667014"},{"key":"ref_62","first-page":"9954","article-title":"Multimodal Emotion Recognition: Emotion Classification through the Integration of EEG and Facial Expressions","volume":"13","author":"Akbulut","year":"2025","journal-title":"IEEE Access"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s40747-024-01762-z","article-title":"A Joint Learning Method for Low-Light Facial Expression Recognition","volume":"11","author":"Xie","year":"2025","journal-title":"Complex Intell. Syst."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"012031","DOI":"10.1088\/1757-899X\/705\/1\/012031","article-title":"Facial Expression Recognition in JAFFE and KDEF Datasets Using Histogram of Oriented Gradients and Support Vector Machine","volume":"705","author":"Eng","year":"2019","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_65","first-page":"139","article-title":"Facial Expression Recognition Using a Hybrid CNN\u2013SIFT Aggregator","volume":"10607","author":"Ang","year":"2017","journal-title":"Multi-disciplinary Trends in Artificial Intelligence"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1954","DOI":"10.1109\/LSP.2020.3031504","article-title":"Fast and Efficient Facial Expression Recognition Using a Gabor Convolutional Network","volume":"27","author":"Jiang","year":"2020","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_67","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 (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/TAFFC.2017.2740923","article-title":"AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild","volume":"10","author":"Mollahosseini","year":"2019","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Barsoum, E., Zhang, C., Ferrer, C.C., and Zhang, Z. (2016, January 12\u201316). Training Deep Networks for Facial Expression Recognition With Crowd-Sourced Label Distribution. Proceedings of the 18th ACM International Conference on Multimodal Interaction (ICMI), Tokyo, Japan.","DOI":"10.1145\/2993148.2993165"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Goodfellow, I.J., Erhan, D., Carrier, P.L., Courville, A., Mirza, M., Hamner, B., Cukierski, W., Tang, Y., Thaler, D., and Lee, D.-H. (2013, January 3\u20137). Challenges in Representation Learning: A Report on Three Machine Learning Contests. Proceedings of the International Conference on Neural Information Processing (ICONIP), Daegu, South Korea.","DOI":"10.1007\/978-3-642-42051-1_16"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Li, S., Deng, W., and Du, J. (2017, January 21\u201326). Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.277"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Nagata, M., and Okajima, K. (2024). Effect of Observer\u2019s Cultural Background and Masking Condition of Target Face on Facial Expression Recognition for Machine-Learning Dataset. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0313029"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Ben Aoun, N. (2024). A Review of Automatic Pain Assessment from Facial Information Using Machine Learning. Technologies, 12.","DOI":"10.3390\/technologies12060092"},{"key":"ref_74","first-page":"495","article-title":"Deep Learning-Based Pain Intensity Estimation from Facial Expressions","volume":"1047","author":"Abraham","year":"2024","journal-title":"Intelligent Systems Design and Applications"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/12\/800\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T05:12:18Z","timestamp":1766121138000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/12\/800"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,17]]},"references-count":74,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["a18120800"],"URL":"https:\/\/doi.org\/10.3390\/a18120800","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2025,12,17]]}}}