{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T16:48:15Z","timestamp":1765039695824,"version":"build-2065373602"},"reference-count":77,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T00:00:00Z","timestamp":1684108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF)","award":["NRF-2018R1D1A3B05049058","NRF-2021R1I1A3A04036408"],"award-info":[{"award-number":["NRF-2018R1D1A3B05049058","NRF-2021R1I1A3A04036408"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human facial emotion detection is one of the challenging tasks in computer vision. Owing to high inter-class variance, it is hard for machine learning models to predict facial emotions accurately. Moreover, a person with several facial emotions increases the diversity and complexity of classification problems. In this paper, we have proposed a novel and intelligent approach for the classification of human facial emotions. The proposed approach comprises customized ResNet18 by employing transfer learning with the integration of triplet loss function (TLF), followed by SVM classification model. Using deep features from a customized ResNet18 trained with triplet loss, the proposed pipeline consists of a face detector used to locate and refine the face bounding box and a classifier to identify the facial expression class of discovered faces. RetinaFace is used to extract the identified face areas from the source image, and a ResNet18 model is trained on cropped face images with triplet loss to retrieve those features. An SVM classifier is used to categorize the facial expression based on the acquired deep characteristics. In this paper, we have proposed a method that can achieve better performance than state-of-the-art (SoTA) methods on JAFFE and MMI datasets. The technique is based on the triplet loss function to generate deep input image features. The proposed method performed well on the JAFFE and MMI datasets with an accuracy of 98.44% and 99.02%, respectively, on seven emotions; meanwhile, the performance of the method needs to be fine-tuned for the FER2013 and AFFECTNET datasets.<\/jats:p>","DOI":"10.3390\/s23104770","type":"journal-article","created":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T02:27:04Z","timestamp":1684204024000},"page":"4770","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Robust Human Face Emotion Classification Using Triplet-Loss-Based Deep CNN Features and SVM"],"prefix":"10.3390","volume":"23","author":[{"given":"Irfan","family":"Haider","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 500-757, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3024-5060","authenticated-orcid":false,"given":"Hyung-Jeong","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 500-757, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8756-1382","authenticated-orcid":false,"given":"Guee-Sang","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 500-757, Republic of Korea"}]},{"given":"Soo-Hyung","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 500-757, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/TSMCB.2005.854502","article-title":"A real-time automated system for the recognition of human facial expressions","volume":"36","author":"Anderson","year":"2006","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"759485","DOI":"10.3389\/fpsyg.2021.759485","article-title":"Facial expression emotion recognition model integrating philosophy and machine learning theory","volume":"12","author":"Song","year":"2021","journal-title":"Front. Psychol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1109\/TIP.2006.884954","article-title":"Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines","volume":"16","author":"Kotsia","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/s12652-021-03003-4","article-title":"Residual attention network for deep face recognition using micro-expression image analysis","volume":"13","author":"Chinnappa","year":"2021","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TAFFC.2014.2386334","article-title":"Automatic facial expression recognition using features of salient facial patches","volume":"6","author":"Happy","year":"2015","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"26756","DOI":"10.1109\/ACCESS.2022.3156598","article-title":"Ad-Corre: Adaptive Correlation-Based Loss for Facial Expression Recognition in the Wild","volume":"10","author":"Fard","year":"2022","journal-title":"IEEE Access"},{"key":"ref_7","unstructured":"Puneet, K., Jain, S., Raman, B., Roy, P.P., and Iwamura, M. (2021, January 10\u201315). End-to-end triplet loss based emotion embedding system for speech emotion recognition. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, W., Chen, X., Zhang, J., and Huang, K. (2017, January 21\u201326). Beyond triplet loss: A deep quadruplet network for person re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.145"},{"key":"ref_9","unstructured":"Hermans, A., Beyer, L., and Leibe, B. (2017). In Defense of the Triplet Loss for Person Re-Identification. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Caleanu, C.-D. (2013, January 23\u201325). Face expression recognition: A brief overview of the last decade. Proceedings of the 2013 IEEE 8th International Symposium on Applied Computational Intelligence and Informatics (SACI), Timisoara, Romania.","DOI":"10.1109\/SACI.2013.6608958"},{"key":"ref_11","unstructured":"Bettadapura, V. (2012). Face Expression Recognition and Analysis: The State of the Art. arXiv."},{"key":"ref_12","unstructured":"Sarath, S. (2020, January 28\u201330). Human emotions recognition from thermal images using Yolo algorithm. Proceedings of the 2020 International Conference on Communication and Signal Processing (ICCSP), Chennai, India."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, H., Zeng, J., Shan, S., and Chen, X. (2020). Emotion recognition for in-the-wild videos. arXiv.","DOI":"10.1109\/FG47880.2020.00102"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, C., Jiang, W., Wang, M., and Tang, T. (2020, January 25\u201329). Group level audio-video emotion recognition using hybrid networks. Proceedings of the 2020 International Conference on Multimodal Interaction, Utrecht, The Netherlands.","DOI":"10.1145\/3382507.3417968"},{"key":"ref_15","first-page":"107","article-title":"Facial Expression Recognition Using 3D Convolutional Neural Network","volume":"5","author":"Byeon","year":"2014","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Song, I., Kim, H.-J., and Jeon, P.B. (2014, January 10\u201313). Deep learning for real-time robust facial expression recognition on a smartphone. Proceedings of the 2014 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE.2014.6776135"},{"key":"ref_17","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 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition\u2014Workshops, San Francisco, CA, USA.","DOI":"10.1109\/CVPRW.2010.5543262"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Zafer, A., Nawaz, R., and Iqbal, J. (2013, January 9\u201310). Face recognition with expression variation via robust NCC. Proceedings of the 2013 IEEE 9th International Conference on Emerging Technologies (ICET), Islamabad, Pakistan.","DOI":"10.1109\/ICET.2013.6743520"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Qi, D., Tan, W., Yao, Q., and Liu, J. (2022). YOLO5Face: Why Reinventing a Face Detector. arXiv.","DOI":"10.1007\/978-3-031-25072-9_15"},{"key":"ref_21","unstructured":"Wei, L., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the Computer Vision\u2014ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part I 14."},{"key":"ref_22","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017). Feature Pyramid Networks for Object Detection. arXiv.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_24","unstructured":"Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., and Xu, J. (2019). MMDetection: Open MMLab Detection Toolbox and Benchmark. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/978-3-030-03000-1_2","article-title":"Facial features detection and localization","volume":"Volume 1","author":"Hassaballah","year":"2019","journal-title":"Recent Advances in Computer Vision: Theories and Applications"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"115636","DOI":"10.1016\/j.image.2019.115636","article-title":"Inter-class angular margin loss for face recognition","volume":"80","author":"Sun","year":"2020","journal-title":"Signal Process. Image Commun."},{"key":"ref_27","unstructured":"Shi, X., Dou, Q., Xue, C., Qin, J., Chen, H., and Heng, P.A. (2019). Machine Learning in Medical Imaging, Proceedings of the 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, 13 October 2019, Proceedings 10, Springer International Publishing."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection Cvfoundation. Proceedings of the the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017). YOLO9000: Better, Faster, Stronger. arXiv.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_30","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_31","unstructured":"Ultralytics (2022, November 15). Yolov5. February 2021. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Khan, M., Chakraborty, S., Astya, R., and Khepra, S. (2019, January 18\u201319). Face Detection and Recognition Using OpenCV. Proceedings of the 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India.","DOI":"10.1109\/ICCCIS48478.2019.8974493"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Venkataramanan, A., Laviale, M., Figus, C., Usseglio-Polatera, P., and Pradalier, C. (2021, January 22\u201324). Tackling inter-class similarity and intra-class variance for microscopic image-based classification. Proceedings of the Computer Vision Systems: 13th International Conference, ICVS 2021, Virtual Event. Proceedings 13.","DOI":"10.1007\/978-3-030-87156-7_8"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lalitha, S.D., and Thyagharajan, K.K. (2020). Micro-facial expression recognition based on deep-rooted learning algorithm. arXiv.","DOI":"10.2991\/ijcis.d.190801.001"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"12868","DOI":"10.1038\/s41598-020-69813-2","article-title":"Developing intelligent medical image modality classification system using deep transfer learning and LDA","volume":"10","author":"Hassan","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_36","unstructured":"(2022, November 01). ImageNet. Available online: http:\/\/www.image-net.org\/challenges\/LSVRC\/2014."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ya\u011f, \u0130., and Altan, A. (2022). Artificial Intelligence-Based Robust Hybrid Algorithm Design and Implementation for Real-Time Detection of Plant Diseases in Agricultural Environments. Biology, 11.","DOI":"10.3390\/biology11121732"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.jesp.2013.03.013","article-title":"Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median","volume":"49","author":"Leys","year":"2013","journal-title":"J. Exp. Soc. Psychol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Zhou, Y., Yu, J., Kotsia, I., and Zafeiriou, S. (2019). RetinaFace: Single-stage Dense Face Localisation in the Wild. arXiv.","DOI":"10.1109\/CVPR42600.2020.00525"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1007\/s00371-020-01831-7","article-title":"YOLO-face: A real-time face detector","volume":"37","author":"Chen","year":"2020","journal-title":"Vis. Comput."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yang, S., Luo, P., Loy, C., and Tang, X. (2016, January 27\u201330). WIDER FACE: A Face Detection Benchmark. Proceedings of the IEEE Conference On Computer Vision And Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.596"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_43","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 2016 IEEE Conference On Computer Vision And Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., and Philbin, J. (2015, January 7\u201312). FaceNet: A unified embedding for face recognition and clustering. Proceedings of the 2015 IEEE Conference On Computer Vision And Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zeiler, M., and Fergus, R. (2013). Visualizing and Understanding Convolutional Networks. arXiv.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.neunet.2014.09.005","article-title":"Challenges in representation learning: A report on three machine learning contests","volume":"64","author":"Goodfellow","year":"2013","journal-title":"Neural Netw. Off. J. Int. Neural Netw. Soc."},{"key":"ref_48","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":"2017","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_49","unstructured":"Pantic, M., Valstar, M., Rademaker, R., and Maat, L. (2005, January 6). Web-based database for facial expression analysis. Proceedings of the 2005 IEEE International Conference On Multimedia And Expo, Amsterdam, The Netherlands."},{"key":"ref_50","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Minaee, S., and Abdolrashidi, A. (2019). Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network. Sensors, 21.","DOI":"10.3390\/s21093046"},{"key":"ref_52","unstructured":"Khaireddin, Y., and Chen, Z. (2021). Facial Emotion Recognition: State of the Art Performance on FER2013. arXiv."},{"key":"ref_53","unstructured":"Aouayeb, M., Hamidouche, W., Soladi\u00e9, C., Kpalma, K., and S\u00e9guier, R. (2021). Learning Vision Transformer with Squeeze and Excitation for Facial Expression Recognition. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s12530-021-09393-2","article-title":"A novel approach for facial expression recognition based on Gabor filters and genetic algorithm","volume":"13","author":"Boughida","year":"2022","journal-title":"Evol. Syst."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Shaik, N., and Cherukuri, T. (2022). Visual attention based composite dense neural network for facial expression recognition. J. Ambient. Intell. Humaniz. Comput., 1\u201314.","DOI":"10.1007\/s12652-022-03843-8"},{"key":"ref_56","unstructured":"Burkert, P., Trier, F., Afzal, M., Dengel, A., and Liwicki, M. (2015). DeXpression: Deep Convolutional Neural Network for Expression Recognition. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wang, J., Chen, S., Shi, Z., and Cai, J. (2019, January 1\u20134). Facial Motion Prior Networks for Facial Expression Recognition. Proceedings of the 2019 IEEE Visual Communications And Image Processing (VCIP), Sydney, NSW, Australia.","DOI":"10.1109\/VCIP47243.2019.8965826"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/j.ins.2022.06.092","article-title":"HistNet: Histogram-based convolutional neural network with Chi-squared deep metric learning for facial expression recognition","volume":"608","author":"Sadeghi","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Han, B., Hu, M., Wang, X., and Ren, F. (2022). A Triple-Structure Network Model Based upon MobileNet V1 and Multi-Loss Function for Facial Expression Recognition. Symmetry, 14.","DOI":"10.3390\/sym14102055"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Vignesh, S., Savithadevi, M., Sridevi, M., and Sridhar, R. (2023). A novel facial emotion recognition model using segmentation VGG-19 architecture. Int. J. Inf. Technol., 1\u201311.","DOI":"10.1007\/s41870-023-01184-z"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Abdulsattar, N., and Hussain, M. (2022, January 15\u201317). Facial Expression Recognition using Transfer Learning and Fine-tuning Strategies: A Comparative Study. Proceedings of the 2022 International Conference On Computer Science And Software Engineering (CSASE), Duhok, Iraq.","DOI":"10.1109\/CSASE51777.2022.9759754"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1007\/s40031-021-00681-8","article-title":"FERNet: A deep CNN architecture for facial expression recognition in the wild","volume":"103","author":"Bodapati","year":"2022","journal-title":"J. Inst. Eng. Ser. B"},{"key":"ref_63","unstructured":"Oguine, O., Oguine, K., Bisallah, H., and Ofuani, D. (2022). Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Classification and Prediction. arXiv."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"16375","DOI":"10.1007\/s11042-022-14191-2","article-title":"NA-Resnet: Neighbor block and optimized attention module for global-local feature extraction in facial expression recognition","volume":"82","author":"Qi","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Pham, L., Vu, T., and Tran, T. (2021, January 10\u201315). Facial Expression Recognition Using Residual Masking Network. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9411919"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Vulpe-Grigoras, I.A., and Grigore, O. (2021, January 25\u201327). Convolutional Neural Network Hyperparameters optimization for Facial Emotion Recognition. Proceedings of the 2021 12th International Symposium On Advanced Topics In Electrical Engineering (ATEE), Bucharest, Romania.","DOI":"10.1109\/ATEE52255.2021.9425073"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"64827","DOI":"10.1109\/ACCESS.2019.2917266","article-title":"Local Learning With Deep and Handcrafted Features for Facial Expression Recognition","volume":"7","author":"Georgescu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Antoniadis, P., Filntisis, P., and Maragos, P. (2021). Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition. arXiv.","DOI":"10.1109\/FG52635.2021.9667014"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/j.neucom.2022.10.013","article-title":"In search of a robust facial expressions recognition model: A large-scale visual cross-corpus study","volume":"514","author":"Ryumina","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Farzaneh, A., and Qi, X. (2021, January 3\u20138). Facial Expression Recognition in theWild via Deep Attentive Center Loss. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00245"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2132","DOI":"10.1109\/TAFFC.2022.3188390","article-title":"Classifying emotions and engagement in online learning based on a single facial expression recognition neural network","volume":"13","author":"Savchenko","year":"2022","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_72","unstructured":"Kollias, D., and Zafeiriou, S. (2019). Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace. arXiv."},{"key":"ref_73","unstructured":"Wen, Z., Lin, W., Wang, T., and Xu, G. (2021). Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognition. arXiv."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Schoneveld, L., Othmani, A., and Abdelkawy, H. (2021). Leveraging Recent Advances in Deep Learning for Audio-Visual Emotion Recognition. arXiv.","DOI":"10.1016\/j.patrec.2021.03.007"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Savchenko, A. (2021, January 16\u201318). Facial expression and attributes recognition based on multi-task learning of lightweight neural networks. Proceedings of the 2021 IEEE 19th International Symposium On Intelligent Systems And Informatics (SISY), Subotica, Serbia.","DOI":"10.1109\/SISY52375.2021.9582508"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Zhou, H., Meng, D., Zhang, Y., Peng, X., Du, J., Wang, K., and Qiao, Y. (2019, January 14\u201318). Exploring Emotion Features and Fusion Strategies for Audio-Video Emotion Recognition. Proceedings of the 2019 International Conference On Multimodal Interaction, Suzhou, China.","DOI":"10.1145\/3340555.3355713"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Siqueira, H., Magg, S., and Wermter, S. (2020). Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks. arXiv.","DOI":"10.1609\/aaai.v34i04.6037"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4770\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:35:36Z","timestamp":1760124936000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4770"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,15]]},"references-count":77,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23104770"],"URL":"https:\/\/doi.org\/10.3390\/s23104770","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,5,15]]}}}