{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T15:55:56Z","timestamp":1792252556585,"version":"4.4.0"},"reference-count":96,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T00:00:00Z","timestamp":1780963200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T00:00:00Z","timestamp":1787270400000},"content-version":"vor","delay-in-days":73,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-56549-8","type":"journal-article","created":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:16:03Z","timestamp":1781018163000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Impact of simulated glasses noise on facial emotion recognition with deep learning models"],"prefix":"10.1038","volume":"16","author":[{"given":"Deniz","family":"Baharvand","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatemeh","family":"Jodati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kimia","family":"Shirini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sina Samadi","family":"Gharehveran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,9]]},"reference":[{"key":"56549_CR1","unstructured":"Ekman, P. & Revealed, E. Recognizing faces and feelings to improve communication and emotional life. Emotions revealed, (2007)."},{"issue":"2","key":"56549_CR2","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1109\/TITS.2005.848368","volume":"6","author":"JA Healey","year":"2005","unstructured":"Healey, J. A. & Picard, R. W. Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans. Intell. Transp. Syst. 6 (2), 156\u2013166 (2005).","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"1","key":"56549_CR3","doi-asserted-by":"publisher","first-page":"6","DOI":"10.3141\/2138-02","volume":"2138","author":"B Mehler","year":"2009","unstructured":"Mehler, B. et al. Impact of incremental increases in cognitive workload on physiological arousal and performance in young adult drivers. Transp. Res. Rec. 2138 (1), 6\u201312 (2009).","journal-title":"Transp. Res. Rec."},{"issue":"7","key":"56549_CR4","doi-asserted-by":"publisher","first-page":"2381","DOI":"10.3390\/s21072381","volume":"21","author":"J Lee","year":"2021","unstructured":"Lee, J., Lee, H. & Shin, M. Driving stress detection using multimodal convolutional neural networks with nonlinear representation of short-term physiological signals. Sensors 21 (7), 2381 (2021).","journal-title":"Sensors"},{"issue":"18","key":"56549_CR5","doi-asserted-by":"publisher","first-page":"26002","DOI":"10.1007\/s11227-024-06407-z","volume":"80","author":"SS Gharehveran","year":"2024","unstructured":"Gharehveran, S. S. et al. Deep learning-based demand response for short-term operation of renewable-based microgrids. J. Supercomputing. 80 (18), 26002\u201326035 (2024).","journal-title":"J. Supercomputing"},{"issue":"4","key":"56549_CR6","doi-asserted-by":"publisher","first-page":"1574","DOI":"10.1007\/s42405-024-00747-z","volume":"25","author":"K Shirini","year":"2024","unstructured":"Shirini, K., Aghdasi, H. S. & Saeedvand, S. A comprehensive survey on multiple-runway aircraft landing optimization problem. Int. J. Aeronaut. Space Sci. 25 (4), 1574\u20131602 (2024).","journal-title":"Int. J. Aeronaut. Space Sci."},{"issue":"3","key":"56549_CR7","first-page":"1","volume":"9","author":"A Taherihajivand","year":"2024","unstructured":"Taherihajivand, A., Shirini, K., Samadi, S. & Gharehveran An overview of product performance prediction using artificial algorithms. J. Agricultural Mechanization. 9 (3), 1\u201314 (2024).","journal-title":"J. Agricultural Mechanization"},{"issue":"3","key":"56549_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3388790","volume":"53","author":"S Zepf","year":"2020","unstructured":"Zepf, S. et al. Driver emotion recognition for intelligent vehicles: A survey. ACM Comput. Surv. (CSUR). 53 (3), 1\u201330 (2020).","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"56549_CR9","doi-asserted-by":"crossref","unstructured":"Busso, C. et al. Analysis of emotion recognition using facial expressions, speech and multimodal information. in Proceedings of the 6th international conference on Multimodal interfaces. (2004).","DOI":"10.1145\/1027933.1027968"},{"key":"56549_CR10","unstructured":"Gharehveran, S. S., Shirini, K. & Abdolahi, A. Optimizing Energy Storage. Energy Storage Devices-A Comprehensive Overview: A Comprehensive Overview, : p. 47. (2025)."},{"key":"56549_CR11","doi-asserted-by":"crossref","unstructured":"Gharehveran, S. S. et al. Optimizing day-ahead power scheduling: a novel MIQCP approach for enhanced SCUC with renewable integration. e-Prime-Advances in Electrical Engineering, Electronics and Energy, : p. 101022. (2025).","DOI":"10.1016\/j.prime.2025.101022"},{"issue":"16","key":"56549_CR12","doi-asserted-by":"publisher","first-page":"1485","DOI":"10.1007\/s11227-025-07983-4","volume":"81","author":"R Nemati","year":"2025","unstructured":"Nemati, R., Shirini, K. & Gharehveran, S. S. FER-HA: a hybrid attention model for facial emotion recognition: R. Nemati et al. J. Supercomputing. 81 (16), 1485 (2025).","journal-title":"J. Supercomputing"},{"key":"56549_CR13","doi-asserted-by":"publisher","first-page":"107063","DOI":"10.1016\/j.bspc.2024.107063","volume":"100","author":"AG Oskouei","year":"2025","unstructured":"Oskouei, A. G. et al. Efficient superpixel-based brain MRI segmentation using multi-scale morphological gradient reconstruction and quantum clustering. Biomed. Signal Process. Control. 100, 107063 (2025).","journal-title":"Biomed. Signal Process. Control"},{"issue":"9","key":"56549_CR14","doi-asserted-by":"publisher","first-page":"1072","DOI":"10.1007\/s11227-025-07505-2","volume":"81","author":"N Saeedi","year":"2025","unstructured":"Saeedi, N. et al. Prediction of electrical energy consumption using principal component analysis and independent components analysis. J. Supercomputing. 81 (9), 1072 (2025).","journal-title":"J. Supercomputing"},{"key":"56549_CR15","unstructured":"Samadi Gharehveran, S., Najafpour, N. & Shirini, K. A Hybrid Approach for Vehicle Detection and Tracking in Low-Visibility Conditions Based on AWBLP, YOLOv8, and GM-PHD Algorithms. J. Mach. Vis. Image Process., : pp. 1\u201311. (2025)."},{"key":"56549_CR16","unstructured":"Samadi Gharehveran, S. & Shirini, K. A review of resilient-oriented operation strategies of electrical energy distribution networks in critical situations (Passive Defense, 2025)."},{"key":"56549_CR17","unstructured":"Samadi Gharehveran, S. et al. A review of game theory-based approaches for demand side management in smart energy grids. Power, Control, and Data Processing Systems, : p. e730417. (2025)."},{"issue":"1","key":"56549_CR18","doi-asserted-by":"publisher","first-page":"15669","DOI":"10.1038\/s41598-025-98235-1","volume":"15","author":"J Tan","year":"2025","unstructured":"Tan, J. et al. Innovative framework for fault detection and system resilience in hydropower operations using digital twins and deep learning. Sci. Rep. 15 (1), 15669 (2025).","journal-title":"Sci. Rep."},{"key":"56549_CR19","doi-asserted-by":"crossref","unstructured":"Barsoum, E. et al. Training deep networks for facial expression recognition with crowd-sourced label distribution. in Proceedings of the 18th ACM international conference on multimodal interaction. (2016).","DOI":"10.1145\/2993148.2993165"},{"key":"56549_CR20","doi-asserted-by":"crossref","unstructured":"Mollahosseini, A., Chan, D. & Mahoor, M. H. Going deeper in facial expression recognition using deep neural networks. in IEEE Winter conference on applications of computer vision (WACV). 2016. IEEE. 2016. IEEE. (2016).","DOI":"10.1109\/WACV.2016.7477450"},{"issue":"5","key":"56549_CR21","doi-asserted-by":"publisher","first-page":"2439","DOI":"10.1109\/TIP.2018.2886767","volume":"28","author":"Y Li","year":"2018","unstructured":"Li, Y. et al. Occlusion aware facial expression recognition using CNN with attention mechanism. IEEE Trans. Image Process. 28 (5), 2439\u20132450 (2018).","journal-title":"IEEE Trans. Image Process."},{"key":"56549_CR22","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1016\/j.ins.2022.11.068","volume":"619","author":"C Liu","year":"2023","unstructured":"Liu, C., Hirota, K. & Dai, Y. Patch attention convolutional vision transformer for facial expression recognition with occlusion. Inf. Sci. 619, 781\u2013794 (2023).","journal-title":"Inf. Sci."},{"key":"56549_CR23","doi-asserted-by":"crossref","unstructured":"Hasani, B. & Mahoor, M. H. Facial expression recognition using enhanced deep 3D convolutional neural networks. in Proceedings of the IEEE conference on computer vision and pattern recognition workshops. (2017).","DOI":"10.1109\/CVPRW.2017.282"},{"issue":"6","key":"56549_CR24","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Commun. ACM. 60 (6), 84\u201390 (2017).","journal-title":"Commun. ACM"},{"key":"56549_CR25","doi-asserted-by":"crossref","unstructured":"He, K. et al. Deep residual learning for image recognition. in Proceedings of the IEEE conference on computer vision and pattern recognition. (2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"56549_CR26","first-page":"1","volume":"72","author":"M Karnati","year":"2023","unstructured":"Karnati, M. et al. Understanding deep learning techniques for recognition of human emotions using facial expressions: A comprehensive survey. IEEE Trans. Instrum. Meas. 72, 1\u201331 (2023).","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"56549_CR27","doi-asserted-by":"crossref","unstructured":"Li, S., Deng, W. & Du, J. Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. in Proceedings of the IEEE conference on computer vision and pattern recognition. (2017).","DOI":"10.1109\/CVPR.2017.277"},{"key":"56549_CR28","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1093\/comjnl\/bxz032","volume":"63","author":"RE Afdhal","year":"2020","unstructured":"Afdhal, R. E. Ridha; Zaied, Mourad, Emotion Recognition by a Hybrid System Based on the Features of Distances and the Shapes of the Wrinkles. Comput. J. 63, 351\u2013363 (2020).","journal-title":"Comput. J."},{"key":"56549_CR29","doi-asserted-by":"crossref","unstructured":"Afdhal, R. E. & Zaied, R. Mourad, Relevant Facial Key Parts and Feature Points for Emotion Recognition, in Proceedings of the 16th International Conference on Pattern Recognition Applications and Methods (ICPRAM). pp. 1270\u20131277. (2024).","DOI":"10.5220\/0012464300003636"},{"key":"56549_CR30","doi-asserted-by":"publisher","first-page":"4057","DOI":"10.1109\/TIP.2019.2956143","volume":"29","author":"K Wang","year":"2020","unstructured":"Wang, K. et al. Region attention networks for pose and occlusion robust facial expression recognition. IEEE Trans. Image Process. 29, 4057\u20134069 (2020).","journal-title":"IEEE Trans. Image Process."},{"key":"56549_CR31","doi-asserted-by":"crossref","unstructured":"Hmidi, N. A., Hamdi, R., Ejbali, M. & Zaied, R. Mourad, Emotion estimation of people wearing masks using machine learning Vol. 19 (INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, 2024). 1.","DOI":"10.15837\/ijccc.2024.1.5363"},{"key":"56549_CR32","doi-asserted-by":"crossref","unstructured":"Huang, Y. et al. FERMixNet: An Occlusion Robust Facial Expression Recognition Model with Facial Mixing Augmentation and Mid-Level Representation Learning (IEEE Transactions on Affective Computing, 2024).","DOI":"10.1109\/TAFFC.2024.3454102"},{"key":"56549_CR33","doi-asserted-by":"crossref","unstructured":"Liu, Z. et al. Swin transformer: Hierarchical vision transformer using shifted windows. in Proceedings of the IEEE\/CVF international conference on computer vision. (2021).","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"3","key":"56549_CR34","doi-asserted-by":"publisher","first-page":"175","DOI":"10.3390\/a16030175","volume":"16","author":"K Saleh","year":"2023","unstructured":"Saleh, K., Sz\u00e9n\u00e1si, S. & V\u00e1mossy, Z. Generative adversarial network for overcoming occlusion in images: A survey. Algorithms 16 (3), 175 (2023).","journal-title":"Algorithms"},{"issue":"10","key":"56549_CR35","doi-asserted-by":"publisher","first-page":"3030","DOI":"10.1109\/TCSVT.2017.2719043","volume":"28","author":"S Zhang","year":"2017","unstructured":"Zhang, S. et al. Learning affective features with a hybrid deep model for audio\u2013visual emotion recognition. IEEE Trans. Circuits Syst. Video Technol. 28 (10), 3030\u20133043 (2017).","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"11","key":"56549_CR36","doi-asserted-by":"publisher","first-page":"5568","DOI":"10.3390\/app12115568","volume":"12","author":"A Khalifa","year":"2022","unstructured":"Khalifa, A. et al. Face recognition and tracking framework for human\u2013robot interaction. Appl. Sci. 12 (11), 5568 (2022).","journal-title":"Appl. Sci."},{"issue":"1","key":"56549_CR37","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/T-AFFC.2010.1","volume":"1","author":"RA Calvo","year":"2010","unstructured":"Calvo, R. A. & D\u2019Mello, S. Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Trans. Affect. Comput. 1 (1), 18\u201337 (2010).","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"12","key":"56549_CR38","doi-asserted-by":"publisher","first-page":"509","DOI":"10.3390\/computers14120509","volume":"14","author":"M Sistaninezhad","year":"2025","unstructured":"Sistaninezhad, M. et al. Classification of Drowsiness and Alertness States Using EEG Signals to Enhance Road Safety: A Comparative Analysis of Machine Learning Algorithms and Ensemble Techniques. Computers 14 (12), 509 (2025).","journal-title":"Computers"},{"issue":"3","key":"56549_CR39","doi-asserted-by":"publisher","first-page":"1195","DOI":"10.1109\/TAFFC.2020.2981446","volume":"13","author":"S Li","year":"2020","unstructured":"Li, S. & Deng, W. Deep facial expression recognition: A survey. IEEE Trans. Affect. Comput. 13 (3), 1195\u20131215 (2020).","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"18","key":"56549_CR40","doi-asserted-by":"publisher","first-page":"8972","DOI":"10.3390\/app12188972","volume":"12","author":"M Shafiq","year":"2022","unstructured":"Shafiq, M. & Gu, Z. Deep residual learning for image recognition: A survey. Appl. Sci. 12 (18), 8972 (2022).","journal-title":"Appl. Sci."},{"key":"56549_CR41","doi-asserted-by":"publisher","first-page":"93594","DOI":"10.1109\/ACCESS.2019.2928125","volume":"7","author":"Y Lu","year":"2019","unstructured":"Lu, Y. et al. Wgan-based robust occluded facial expression recognition. IEEE Access. 7, 93594\u201393610 (2019).","journal-title":"IEEE Access."},{"key":"56549_CR42","doi-asserted-by":"crossref","unstructured":"Yin, B. et al. Towards interpretable face recognition. in Proceedings of the IEEE\/CVF international conference on computer vision. (2019).","DOI":"10.1109\/ICCV.2019.00944"},{"issue":"7","key":"56549_CR43","doi-asserted-by":"publisher","first-page":"3015","DOI":"10.1109\/JBHI.2022.3157722","volume":"26","author":"G Lu","year":"2022","unstructured":"Lu, G. et al. Landmark localization for cephalometric analysis using multiscale image patch-based graph convolutional networks. IEEE J. Biomedical Health Inf. 26 (7), 3015\u20133024 (2022).","journal-title":"IEEE J. Biomedical Health Inf."},{"key":"56549_CR44","doi-asserted-by":"crossref","unstructured":"Li, Y. et al. Patch-gated CNN for occlusion-aware facial expression recognition. in. 24th international conference on pattern recognition (ICPR). 2018. IEEE. (2018).","DOI":"10.1109\/ICPR.2018.8545853"},{"key":"56549_CR45","doi-asserted-by":"crossref","unstructured":"Kim, J., Yang, J. & Tong, X. Learning high-fidelity face texture completion without complete face texture. in Proceedings of the IEEE\/CVF International Conference on Computer Vision. (2021).","DOI":"10.1109\/ICCV48922.2021.01373"},{"issue":"3","key":"56549_CR46","doi-asserted-by":"publisher","first-page":"3576","DOI":"10.1109\/TASE.2023.3281394","volume":"21","author":"J Zhang","year":"2023","unstructured":"Zhang, J., Cheng, Y. & He, X. Fault diagnosis of energy networks based on improved spatial\u2013temporal graph neural network with massive missing data. IEEE Trans. Autom. Sci. Eng. 21 (3), 3576\u20133587 (2023).","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"issue":"06","key":"56549_CR47","doi-asserted-by":"publisher","first-page":"2350032","DOI":"10.1142\/S0129065723500326","volume":"33","author":"Y Yang","year":"2023","unstructured":"Yang, Y. et al. Facial expression recognition with contrastive learning and uncertainty-guided relabeling. Int. J. Neural Syst. 33 (06), 2350032 (2023).","journal-title":"Int. J. Neural Syst."},{"key":"56549_CR48","doi-asserted-by":"crossref","unstructured":"Zhai, H. et al. Rethinking Occlusion in FER: A Semantic-Aware Perspective and Go Beyond. in Proceedings of the 33rd ACM International Conference on Multimedia. (2025).","DOI":"10.1145\/3746027.3754978"},{"issue":"4","key":"56549_CR49","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.3390\/app14041535","volume":"14","author":"L Xiong","year":"2024","unstructured":"Xiong, L. et al. Context transformer and adaptive method with visual transformer for robust facial expression recognition. Appl. Sci. 14 (4), 1535 (2024).","journal-title":"Appl. Sci."},{"key":"56549_CR50","doi-asserted-by":"crossref","unstructured":"Pfister, T. et al. Recognising spontaneous facial micro-expressions. in 2011 international conference on computer vision. IEEE. (2011).","DOI":"10.1109\/ICCV.2011.6126401"},{"key":"56549_CR51","doi-asserted-by":"publisher","first-page":"5573","DOI":"10.1109\/ACCESS.2020.3046715","volume":"9","author":"N Zhou","year":"2020","unstructured":"Zhou, N., Liang, R. & Shi, W. A lightweight convolutional neural network for real-time facial expression detection. IEEE Access. 9, 5573\u20135584 (2020).","journal-title":"IEEE Access."},{"key":"56549_CR52","doi-asserted-by":"crossref","unstructured":"Sudha, S. & Suganya, S. On-road driver facial expression emotion recognition with parallel multi-verse optimizer (PMVO) and optical flow reconstruction for partial occlusion in internet of things (IoT) Vol. 26, p. 100711 (Sensors, 2023).","DOI":"10.1016\/j.measen.2023.100711"},{"key":"56549_CR53","doi-asserted-by":"crossref","unstructured":"Van Gemert, T. & Bergstr\u00f6m, J. Evaluating vr sickness in vr locomotion techniques. in 2021 IEEE conference on virtual reality and 3D user interfaces abstracts and workshops (VRW). IEEE. (2021).","DOI":"10.1109\/VRW52623.2021.00078"},{"key":"56549_CR54","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.neucom.2020.06.014","volume":"411","author":"J Li","year":"2020","unstructured":"Li, J. et al. Attention mechanism-based CNN for facial expression recognition. Neurocomputing 411, 340\u2013350 (2020).","journal-title":"Neurocomputing"},{"issue":"4","key":"56549_CR55","first-page":"429","volume":"36","author":"Y Xue","year":"2010","unstructured":"Xue, Y. et al. Robust facial expression recognition under occlusion condition. J. Beijing Univ. Aeronaut. Astronaut. 36 (4), 429 (2010).","journal-title":"J. Beijing Univ. Aeronaut. Astronaut."},{"issue":"11","key":"56549_CR56","doi-asserted-by":"publisher","first-page":"2583","DOI":"10.1109\/TPAMI.2018.2791608","volume":"40","author":"W Li","year":"2018","unstructured":"Li, W. et al. Eac-net: Deep nets with enhancing and cropping for facial action unit detection. IEEE Trans. Pattern Anal. Mach. Intell. 40 (11), 2583\u20132596 (2018).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"56549_CR57","doi-asserted-by":"publisher","first-page":"5497","DOI":"10.1007\/s10489-021-02728-1","volume":"52","author":"HN Vu","year":"2022","unstructured":"Vu, H. N., Nguyen, M. H. & Pham, C. Masked face recognition with convolutional neural networks and local binary patterns. Appl. Intell. 52 (5), 5497\u20135512 (2022).","journal-title":"Appl. Intell."},{"issue":"2","key":"56549_CR58","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1109\/TBIOM.2023.3242085","volume":"5","author":"Z Wang","year":"2023","unstructured":"Wang, Z. et al. Masked face recognition dataset and application. IEEE Trans. Biometrics Behav. Identity Sci. 5 (2), 298\u2013304 (2023).","journal-title":"IEEE Trans. Biometrics Behav. Identity Sci."},{"key":"56549_CR59","doi-asserted-by":"crossref","unstructured":"Huber, M. et al. Mask-invariant face recognition through template-level knowledge distillation. in. 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021). 2021. IEEE. (2021).","DOI":"10.1109\/FG52635.2021.9667081"},{"issue":"7","key":"56549_CR60","doi-asserted-by":"publisher","first-page":"1052","DOI":"10.1016\/j.imavis.2007.11.004","volume":"26","author":"I Kotsia","year":"2008","unstructured":"Kotsia, I., Buciu, I. & Pitas, I. An analysis of facial expression recognition under partial facial image occlusion. Image Vis. Comput. 26 (7), 1052\u20131067 (2008).","journal-title":"Image Vis. Comput."},{"key":"56549_CR61","doi-asserted-by":"crossref","unstructured":"Houshmand, B. & Khan, N. M. Facial expression recognition under partial occlusion from virtual reality headsets based on transfer learning. in IEEE Sixth International Conference on Multimedia Big Data (BigMM). 2020. IEEE. 2020. IEEE. (2020).","DOI":"10.1109\/BigMM50055.2020.00020"},{"key":"56549_CR62","doi-asserted-by":"crossref","unstructured":"Tang, H. et al. Multimodal emotion recognition using deep neural networks. in International Conference on Neural Information Processing. Springer. (2017).","DOI":"10.1007\/978-3-319-70093-9_86"},{"key":"56549_CR63","doi-asserted-by":"crossref","unstructured":"Li, Y. et al. Mec 2017: Multimodal emotion recognition challenge. in. first Asian conference on affective computing and intelligent interaction (ACII Asia). 2018. IEEE. (2018).","DOI":"10.1109\/ACIIAsia.2018.8470342"},{"key":"56549_CR64","doi-asserted-by":"crossref","unstructured":"Kortelainen, J. et al. Multimodal emotion recognition by combining physiological signals and facial expressions: a preliminary study. in. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2012. IEEE. (2012).","DOI":"10.1109\/EMBC.2012.6347175"},{"key":"56549_CR65","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.entcs.2019.04.009","volume":"343","author":"M Egger","year":"2019","unstructured":"Egger, M., Ley, M. & Hanke, S. Emotion recognition from physiological signal analysis: A review. Electron. Notes Theor. Comput. Sci. 343, 35\u201355 (2019).","journal-title":"Electron. Notes Theor. Comput. Sci."},{"key":"56549_CR66","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.eswa.2018.06.014","volume":"112","author":"H Feng","year":"2018","unstructured":"Feng, H., Golshan, H. M. & Mahoor, M. H. A wavelet-based approach to emotion classification using EDA signals. Expert Syst. Appl. 112, 77\u201386 (2018).","journal-title":"Expert Syst. Appl."},{"key":"56549_CR67","doi-asserted-by":"crossref","unstructured":"Xia, V. et al. Active learning for electrodermal activity classification. in 2015 ieee signal processing in medicine and biology symposium (spmb). IEEE. (2015).","DOI":"10.1109\/SPMB.2015.7405467"},{"key":"56549_CR68","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.patrec.2014.11.007","volume":"66","author":"F Ringeval","year":"2015","unstructured":"Ringeval, F. et al. Prediction of asynchronous dimensional emotion ratings from audiovisual and physiological data. Pattern Recognit. Lett. 66, 22\u201330 (2015).","journal-title":"Pattern Recognit. Lett."},{"key":"56549_CR69","unstructured":"Koelstra, R. A. L. Affective and Implicit Tagging using Facial Expressions and Electroencephalography (Queen Mary University of London, 2012)."},{"key":"56549_CR70","doi-asserted-by":"crossref","unstructured":"Chen, C., Sun, K. & Zhang, X. Exgsense: Toward facial gesture sensing with a sparse near-eye sensor array. in Proceedings of the 20th International Conference on Information Processing in Sensor Networks (co-located with CPS-IoT Week 2021). (2021).","DOI":"10.1145\/3412382.3458268"},{"issue":"1","key":"56549_CR71","first-page":"356","volume":"28","author":"L Shan","year":"2018","unstructured":"Shan, L. & Deng, W. Reliable crowdsourcing and deep locality-preserving learning for unconstrained facial expression recognition. IEEE Trans. Image Process. 28 (1), 356\u2013370 (2018).","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"56549_CR72","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3158369","volume":"51","author":"L Zhang","year":"2018","unstructured":"Zhang, L. et al. Facial expression analysis under partial occlusion: A survey. ACM Comput. Surv. (CSUR). 51 (2), 1\u201349 (2018).","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"56549_CR73","first-page":"12116","volume":"34","author":"M Raghu","year":"2021","unstructured":"Raghu, M. et al. Do vision transformers see like convolutional neural networks? Adv. Neural. Inf. Process. Syst. 34, 12116\u201312128 (2021).","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"16","key":"56549_CR74","doi-asserted-by":"publisher","first-page":"7092","DOI":"10.3390\/s23167092","volume":"23","author":"AL C\u00eerneanu","year":"2023","unstructured":"C\u00eerneanu, A. L., Popescu, D. & Iordache, D. New trends in emotion recognition using image analysis by neural networks, a systematic review. Sensors 23 (16), 7092 (2023).","journal-title":"Sensors"},{"issue":"1","key":"56549_CR75","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1109\/TAFFC.2021.3053275","volume":"14","author":"J Deng","year":"2021","unstructured":"Deng, J. & Ren, F. A survey of textual emotion recognition and its challenges. IEEE Trans. Affect. Comput. 14 (1), 49\u201367 (2021).","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"8","key":"56549_CR76","doi-asserted-by":"publisher","first-page":"1548","DOI":"10.1109\/TPAMI.2016.2515606","volume":"38","author":"CA Corneanu","year":"2016","unstructured":"Corneanu, C. A. et al. Survey on rgb, 3d, thermal, and multimodal approaches for facial expression recognition: History, trends, and affect-related applications. IEEE Trans. Pattern Anal. Mach. Intell. 38 (8), 1548\u20131568 (2016).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"56549_CR77","doi-asserted-by":"crossref","unstructured":"She, J. et al. Dive into ambiguity: Latent distribution mining and pairwise uncertainty estimation for facial expression recognition. in Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. (2021).","DOI":"10.1109\/CVPR46437.2021.00618"},{"key":"56549_CR78","unstructured":"Wang, Y. et al. A survey on facial expression recognition of static and dynamic emotions. arXiv preprint arXiv:2408.15777, (2024)."},{"key":"56549_CR79","doi-asserted-by":"crossref","unstructured":"Ekman, P. & Friesen, W. V. Facial action coding system (Environmental Psychology & Nonverbal Behavior, 1978).","DOI":"10.1037\/t27734-000"},{"key":"56549_CR80","doi-asserted-by":"publisher","first-page":"64827","DOI":"10.1109\/ACCESS.2019.2917266","volume":"7","author":"MI Georgescu","year":"2019","unstructured":"Georgescu, M. I., Ionescu, R. T. & Popescu, M. Local learning with deep and handcrafted features for facial expression recognition. IEEE Access. 7, 64827\u201364836 (2019).","journal-title":"IEEE Access."},{"issue":"1","key":"56549_CR81","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C. & Khoshgoftaar, T. M. A survey on image data augmentation for deep learning. J. big data. 6 (1), 1\u201348 (2019).","journal-title":"J. big data"},{"key":"56549_CR82","doi-asserted-by":"publisher","first-page":"763100","DOI":"10.3389\/fnbot.2021.763100","volume":"15","author":"J Gao","year":"2021","unstructured":"Gao, J. & Zhao, Y. Tfe: A transformer architecture for occlusion aware facial expression recognition. Front. Neurorobotics. 15, 763100 (2021).","journal-title":"Front. Neurorobotics."},{"key":"56549_CR83","doi-asserted-by":"publisher","first-page":"134051","DOI":"10.1109\/ACCESS.2020.3007109","volume":"8","author":"M Val-Calvo","year":"2020","unstructured":"Val-Calvo, M. et al. Affective robot story-telling human-robot interaction: exploratory real-time emotion estimation analysis using facial expressions and physiological signals. IEEE Access. 8, 134051\u2013134066 (2020).","journal-title":"IEEE Access."},{"key":"56549_CR84","unstructured":"Dosovitskiy, A. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, (2020)."},{"key":"56549_CR85","unstructured":"Powers, D. M. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061, (2020)."},{"issue":"12","key":"56549_CR86","doi-asserted-by":"publisher","first-page":"5349","DOI":"10.1109\/TNNLS.2020.2966319","volume":"31","author":"F He","year":"2020","unstructured":"He, F., Liu, T. & Tao, D. Why resnet works? residuals generalize. IEEE Trans. neural networks Learn. Syst. 31 (12), 5349\u20135362 (2020).","journal-title":"IEEE Trans. neural networks Learn. Syst."},{"key":"56549_CR87","doi-asserted-by":"crossref","unstructured":"Woo, S. et al. Cbam: Convolutional block attention module. in Proceedings of the European conference on computer vision (ECCV). (2018).","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"56549_CR88","doi-asserted-by":"crossref","unstructured":"Cai, H. et al. Efficient architecture search by network transformation. in Proceedings of the AAAI conference on artificial intelligence. (2018).","DOI":"10.1609\/aaai.v32i1.11709"},{"issue":"1","key":"56549_CR89","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1186\/s12911-023-02289-y","volume":"23","author":"Y Gheibi","year":"2023","unstructured":"Gheibi, Y. et al. CNN-Res: deep learning framework for segmentation of acute ischemic stroke lesions on multimodal MRI images. BMC Med. Inf. Decis. Mak. 23 (1), 192 (2023).","journal-title":"BMC Med. Inf. Decis. Mak."},{"key":"56549_CR90","doi-asserted-by":"crossref","unstructured":"Touvron, H. et al. Training data-efficient image transformers & distillation through attention. in International conference on machine learning. PMLR. (2021).","DOI":"10.1109\/ICCV48922.2021.00010"},{"key":"56549_CR91","doi-asserted-by":"crossref","unstructured":"Liu, Z. et al. Video swin transformer. in Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. (2022).","DOI":"10.1109\/CVPR52688.2022.00320"},{"key":"56549_CR92","doi-asserted-by":"crossref","unstructured":"Deng, J. et al. Imagenet: A large-scale hierarchical image database. in 2009 IEEE conference on computer vision and pattern recognition. Ieee. (2009).","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"56549_CR93","unstructured":"Loshchilov, I. & Hutter, F. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, (2017)."},{"key":"56549_CR94","doi-asserted-by":"crossref","unstructured":"Yun, S. et al. Cutmix: Regularization strategy to train strong classifiers with localizable features. in Proceedings of the IEEE\/CVF international conference on computer vision. (2019).","DOI":"10.1109\/ICCV.2019.00612"},{"key":"56549_CR95","unstructured":"Lopez-Paz, Y. N. D. D., Zhang, H. & Cisse, M. mixup: Beyond empirical risk minimization. in International Conference on Learning Representations. (2018)."},{"issue":"1","key":"56549_CR96","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/TAFFC.2017.2740923","volume":"10","author":"A Mollahosseini","year":"2017","unstructured":"Mollahosseini, A., Hasani, B. & Mahoor, M. H. Affectnet: A database for facial expression, valence, and arousal computing in the wild. IEEE Trans. Affect. Comput. 10 (1), 18\u201331 (2017).","journal-title":"IEEE Trans. Affect. Comput."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-56549-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-56549-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-56549-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T00:03:46Z","timestamp":1787270626000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-56549-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,9]]},"references-count":96,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["56549"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-56549-8","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,9]]},"assertion":[{"value":"14 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","label":"Ethical and informed consent","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"26253"}}