{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T07:26:38Z","timestamp":1778311598725,"version":"3.51.4"},"reference-count":102,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T00:00:00Z","timestamp":1712016000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T00:00:00Z","timestamp":1712016000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-19012-2","type":"journal-article","created":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T05:02:12Z","timestamp":1712034132000},"page":"10027-10069","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["An integrated attention-guided deep convolutional neural network for facial expression recognition in the wild"],"prefix":"10.1007","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4375-4107","authenticated-orcid":false,"given":"Sumeet","family":"Saurav","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ravi","family":"Saini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanjay","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,2]]},"reference":[{"key":"19012_CR1","doi-asserted-by":"crossref","first-page":"93998","DOI":"10.1109\/ACCESS.2019.2928364","volume":"7","author":"THS Li","year":"2019","unstructured":"Li THS, Kuo PH, Tsai TN, Luan PC (2019) Cnn and lstm based facial expression analysis model for a humanoid robot. IEEE Access 7:93998\u201394011","journal-title":"IEEE Access"},{"key":"19012_CR2","unstructured":"Wu M, Su W, Chen L, Liu Z, Cao W, Hirota K (2019) Weight-adapted convolution neural network for facial expression recognition in human-robot interaction. IEEE Trans Syst Man Cybern: Syst"},{"issue":"3","key":"19012_CR3","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1504\/IJCVR.2016.077353","volume":"6","author":"NT Cao","year":"2016","unstructured":"Cao NT, Ton-That AH, Choi HI (2016) An effective facial expression recognition approach for intelligent game systems. Int J Comput Vis Robot 6(3):223\u2013234","journal-title":"Int J Comput Vis Robot"},{"issue":"12","key":"19012_CR4","doi-asserted-by":"crossref","first-page":"4270","DOI":"10.3390\/s18124270","volume":"18","author":"M Jeong","year":"2018","unstructured":"Jeong M, Ko BC (2018) Driver\u2019s facial expression recognition in real-time for safe driving. Sensors 18(12):4270","journal-title":"Sensors"},{"issue":"4","key":"19012_CR5","doi-asserted-by":"crossref","first-page":"957","DOI":"10.3390\/s18040957","volume":"18","author":"KW Lee","year":"2018","unstructured":"Lee KW, Yoon HS, Song JM, Park KR (2018) Convolutional neural network-based classification of driver\u2019s emotion during aggressive and smooth driving using multi-modal camera sensors. Sensors 18(4):957","journal-title":"Sensors"},{"issue":"4","key":"19012_CR6","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1007\/s12652-017-0464-x","volume":"8","author":"E Lozano-Monasor","year":"2017","unstructured":"Lozano-Monasor E, L\u00f3pez MT, Vigo-Bustos F, Fern\u00e1ndez-Caballero A (2017) Facial expression recognition in ageing adults: from lab to ambient assisted living. J Ambient Intell Humaniz Comput 8(4):567\u2013578","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"19012_CR7","doi-asserted-by":"crossref","first-page":"226437","DOI":"10.1109\/ACCESS.2020.3046225","volume":"8","author":"K Zheng","year":"2020","unstructured":"Zheng K, Yang D, Liu J, Cui J (2020) Recognition of teachers\u2019 facial expression intensity based on convolutional neural network and attention mechanism. IEEE Access 8:226437\u2013226444","journal-title":"IEEE Access"},{"issue":"14","key":"19012_CR8","doi-asserted-by":"crossref","first-page":"18943","DOI":"10.1007\/s11042-019-7250-z","volume":"78","author":"K Bahreini","year":"2019","unstructured":"Bahreini K, van der Vegt W, Westera W (2019) A fuzzy logic approach to reliable real-time recognition of facial emotions. Multimed Tools Appl 78(14):18943\u201318966","journal-title":"Multimed Tools Appl"},{"key":"19012_CR9","doi-asserted-by":"crossref","unstructured":"Meshach WT, Hemajothi S, Anita EM (2020) Real-time facial expression recognition for affect identification using multi-dimensional svm. J Ambient Intell Humaniz Comput, pp 1\u201311","DOI":"10.1007\/s12652-020-02221-6"},{"key":"19012_CR10","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.neucom.2020.01.034","volume":"388","author":"Z Fei","year":"2020","unstructured":"Fei Z, Yang E, Li DDU, Butler S, Ijomah W, Li X, Zhou H (2020) Deep convolution network based emotion analysis towards mental health care. Neurocomputing 388:212\u2013227","journal-title":"Neurocomputing"},{"key":"19012_CR11","doi-asserted-by":"crossref","unstructured":"Vergura DT, Luceri B (2018) Product packaging and consumers\u2019 emotional response. does spatial representation influence product evaluation and choice? J Consum Mark","DOI":"10.1108\/JCM-12-2016-2021"},{"issue":"1","key":"19012_CR12","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s12652-019-01310-5","volume":"11","author":"G Yolcu","year":"2020","unstructured":"Yolcu G, Oztel I, Kazan S, Oz C, Bunyak F (2020) Deep learning-based face analysis system for monitoring customer interest. J Ambient Intell Humaniz Comput 11(1):237\u2013248","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"19012_CR13","doi-asserted-by":"crossref","unstructured":"Bartkiene E, Steibliene V, Adomaitiene V, Juodeikiene G, Cernauskas D, Lele V, Klupsaite D, Zadeike D, Jarutiene L, Guin\u00e9 RP (2019) Factors affecting consumer food preferences: food taste and depression-based evoked emotional expressions with the use of face reading technology. Biomed Res Int 2019","DOI":"10.1155\/2019\/2097415"},{"key":"19012_CR14","doi-asserted-by":"crossref","unstructured":"Saurav S, Saini AK, Saini R, Singh S (2021) Deep learning inspired intelligent embedded system for haptic rendering of facial emotions to the blind. Neural Comput Appl, pp 1\u201329","DOI":"10.1007\/s00521-021-06613-3"},{"key":"19012_CR15","doi-asserted-by":"crossref","first-page":"110421","DOI":"10.1109\/ACCESS.2021.3102042","volume":"9","author":"VS Bawa","year":"2021","unstructured":"Bawa VS, Sharma S, Usman M, Gupta A, Kumar V (2021) An automatic multimedia likability prediction system based on facial expression of observer. IEEE Access 9:110421\u2013110434","journal-title":"IEEE Access"},{"issue":"8","key":"19012_CR16","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1002\/acm2.12945","volume":"21","author":"KH Kim","year":"2020","unstructured":"Kim KH, Park K, Kim H, Jo B, Ahn SH, Kim C, Kim M, Kim TH, Lee SB, Shin D et al (2020) Facial expression monitoring system for predicting patient\u2019s sudden movement during radiotherapy using deep learning. J Appl Clin Med Phys 21(8):191\u2013199","journal-title":"J Appl Clin Med Phys"},{"key":"19012_CR17","doi-asserted-by":"crossref","unstructured":"Chen L, Ma X, Zhu N, Xue H, Zeng H, Chen H, Wang X (2021) Facial expression recognition with machine learning and assessment of distress in patients with cancer. In: Oncology Nursing Forum, vol 48, pp 81\u201393","DOI":"10.1188\/21.ONF.81-93"},{"key":"19012_CR18","doi-asserted-by":"crossref","first-page":"4057","DOI":"10.1109\/TIP.2019.2956143","volume":"29","author":"K Wang","year":"2020","unstructured":"Wang K, Peng X, Yang J, Meng D, Qiao Y (2020) Region attention networks for pose and occlusion robust facial expression recognition. IEEE Trans Image Process 29:4057\u20134069","journal-title":"IEEE Trans Image Process"},{"key":"19012_CR19","doi-asserted-by":"crossref","first-page":"131988","DOI":"10.1109\/ACCESS.2020.3010018","volume":"8","author":"TH Vo","year":"2020","unstructured":"Vo TH, Lee GS, Yang HJ, Kim SH (2020) Pyramid with super resolution for in-the-wild facial expression recognition. IEEE Access 8:131988\u2013132001","journal-title":"IEEE Access"},{"key":"19012_CR20","doi-asserted-by":"crossref","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 (2023) Patch attention convolutional vision transformer for facial expression recognition with occlusion. Inf Sci 619:781\u2013794","journal-title":"Inf Sci"},{"issue":"4","key":"19012_CR21","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.3390\/s20041087","volume":"20","author":"MN Riaz","year":"2020","unstructured":"Riaz MN, Shen Y, Sohail M, Guo M (2020) Exnet: an efficient approach for emotion recognition in the wild. Sensors 20(4):1087","journal-title":"Sensors"},{"key":"19012_CR22","doi-asserted-by":"crossref","unstructured":"Gera D, Balasubramanian S (2021) Imponderous net for facial expression recognition in the wild. arXiv:2103.15136","DOI":"10.1016\/j.patrec.2022.01.013"},{"issue":"9","key":"19012_CR23","doi-asserted-by":"crossref","first-page":"6529","DOI":"10.1007\/s00521-022-08040-4","volume":"35","author":"W Gong","year":"2023","unstructured":"Gong W, Qian Y, Fan Y (2023) Mpcsan: multi-head parallel channel-spatial attention network for facial expression recognition in the wild. Neural Comput Appl 35(9):6529\u20136543","journal-title":"Neural Comput Appl"},{"key":"19012_CR24","doi-asserted-by":"crossref","unstructured":"Li C, Li X, Wang X, Huang D, Liu Z, Liao L (2023) Fg-agr: fine-grained associative graph representation for facial expression recognition in the wild. IEEE Trans Circ Syst Video Tech","DOI":"10.1109\/TCSVT.2023.3237006"},{"issue":"12","key":"19012_CR25","doi-asserted-by":"crossref","first-page":"10175","DOI":"10.1007\/s00521-022-07016-8","volume":"34","author":"W Gong","year":"2022","unstructured":"Gong W, Fan Y, Qian Y (2022) Effective attention feature reconstruction loss for facial expression recognition in the wild. Neural Comput Appl 34(12):10175\u201310187","journal-title":"Neural Comput Appl"},{"issue":"9","key":"19012_CR26","doi-asserted-by":"crossref","first-page":"5595","DOI":"10.1007\/s11042-019-08422-2","volume":"79","author":"H Ling","year":"2020","unstructured":"Ling H, Wu J, Huang J, Chen J, Li P (2020) Attention-based convolutional neural network for deep face recognition. Multimed Tools Appl 79(9):5595\u20135616","journal-title":"Multimed Tools Appl"},{"key":"19012_CR27","doi-asserted-by":"crossref","unstructured":"Saurav S, Gidde P, Saini R, Singh S (2021) Dual integrated convolutional neural network for real-time facial expression recognition in the wild. Vis Comput, pp 1\u201314","DOI":"10.1007\/s00371-021-02069-7"},{"issue":"2","key":"19012_CR28","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s12530-021-09393-2","volume":"13","author":"A Boughida","year":"2022","unstructured":"Boughida A, Kouahla MN, Lafifi Y (2022) A novel approach for facial expression recognition based on gabor filters and genetic algorithm. Evol Syst 13(2):331\u2013345","journal-title":"Evol Syst"},{"key":"19012_CR29","doi-asserted-by":"crossref","unstructured":"Hao M, Yuan F, Li J, Sun Y (2023) Facial expression recognition based on regional adaptive correlation. IET Comput Vis","DOI":"10.1049\/cvi2.12179"},{"key":"19012_CR30","doi-asserted-by":"crossref","unstructured":"Chen D, Wen G, Li H, Chen R, Li C (2023) Multi-relations aware network for in-the-wild facial expression recognition. IEEE Trans Circ Syst Video Tech","DOI":"10.1109\/TCSVT.2023.3234312"},{"key":"19012_CR31","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.ins.2023.02.056","volume":"630","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Tian X, Zhang Y, Guo K, Xu X (2023) Enhanced discriminative global-local feature learning with priority for facial expression recognition. Inf Sci 630:370\u2013384","journal-title":"Inf Sci"},{"key":"19012_CR32","doi-asserted-by":"crossref","unstructured":"Xiao J, Gan C, Zhu Q, Zhu Y, Liu G (2023) Cfnet: Facial expression recognition via constraint fusion under multi-task joint learning network. Appl Soft Comput, p 110312","DOI":"10.1016\/j.asoc.2023.110312"},{"key":"19012_CR33","doi-asserted-by":"crossref","unstructured":"Li Y, Gao Y, Chen B, Zhang Z, Lu G, Zhang D (2021) Self-supervised exclusive-inclusive interactive learning for multi-label facial expression recognition in the wild. IEEE Trans Circ Syst Video Tech","DOI":"10.1109\/TCSVT.2021.3103782"},{"issue":"1","key":"19012_CR34","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1007\/s10489-020-01855-5","volume":"51","author":"J Shao","year":"2021","unstructured":"Shao J, Cheng Q (2021) E-fcnn for tiny facial expression recognition. Appl Intell 51(1):549\u2013559","journal-title":"Appl Intell"},{"key":"19012_CR35","doi-asserted-by":"crossref","unstructured":"Reddy AH, Kolli K, Kiran YL (2021) Deep cross feature adaptive network for facial emotion classification. Signal, image and video processing, pp 1\u20138","DOI":"10.1007\/s11760-021-01941-2"},{"key":"19012_CR36","doi-asserted-by":"crossref","unstructured":"Gera D, Balasubramanian S (2021a) Noisy annotations robust consensual collaborative affect expression recognition. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3585\u20133592","DOI":"10.1109\/ICCVW54120.2021.00399"},{"key":"19012_CR37","doi-asserted-by":"crossref","unstructured":"Gera D, Balasubramanian S (2021b) Consensual collaborative training and knowledge distillation based facial expression recognition under noisy annotations. arXiv:2107.04746","DOI":"10.14445\/22315381\/IJETT-V69I7P231"},{"key":"19012_CR38","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.patrec.2021.01.029","volume":"145","author":"D Gera","year":"2021","unstructured":"Gera D, Balasubramanian S (2021) Landmark guidance independent spatio-channel attention and complementary context information based facial expression recognition. Pattern Recogn Lett 145:58\u201366","journal-title":"Pattern Recogn Lett"},{"key":"19012_CR39","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.neucom.2020.12.076","volume":"432","author":"H Li","year":"2021","unstructured":"Li H, Wang N, Yu Y, Yang X, Gao X (2021) Lban-il: a novel method of high discriminative representation for facial expression recognition. Neurocomputing 432:159\u2013169","journal-title":"Neurocomputing"},{"key":"19012_CR40","volume":"115","author":"B Chen","year":"2021","unstructured":"Chen B, Guan W, Li P, Ikeda N, Hirasawa K, Lu H (2021) Residual multi-task learning for facial landmark localization and expression recognition. Pattern Recogn 115:107893","journal-title":"Pattern Recogn"},{"key":"19012_CR41","unstructured":"Li M, Xu H, Huang X, Song Z, Liu X, Li X (2018) Facial expression recognition with identity and emotion joint learning. IEEE Trans Affect Comput"},{"issue":"10","key":"19012_CR42","doi-asserted-by":"crossref","first-page":"11382","DOI":"10.1109\/JSEN.2020.2997182","volume":"21","author":"W Yang","year":"2020","unstructured":"Yang W, Gao H, Jiang Y, Yu J, Sun J, Liu J, Ju Z (2020) A cascaded feature pyramid network with non-backward propagation for facial expression recognition. IEEE Sens J 21(10):11382\u201311392","journal-title":"IEEE Sens J"},{"key":"19012_CR43","doi-asserted-by":"crossref","first-page":"50321","DOI":"10.1109\/ACCESS.2021.3069468","volume":"9","author":"W Su","year":"2021","unstructured":"Su W, Zhang H, Su Y, Yu J (2021) Facial expression recognition with confidence guided refined horizontal pyramid network. IEEE Access 9:50321\u201350331","journal-title":"IEEE Access"},{"key":"19012_CR44","doi-asserted-by":"crossref","first-page":"39255","DOI":"10.1109\/ACCESS.2021.3063493","volume":"9","author":"C Shi","year":"2021","unstructured":"Shi C, Tan C, Wang L (2021) A facial expression recognition method based on a multibranch cross-connection convolutional neural network. IEEE Access 9:39255\u201339274","journal-title":"IEEE Access"},{"key":"19012_CR45","doi-asserted-by":"crossref","unstructured":"Yu W, Xu H (2021) Co-attentive multi-task convolutional neural network for facial expression recognition. Pattern Recogn, p 108401","DOI":"10.1016\/j.patcog.2021.108401"},{"key":"19012_CR46","doi-asserted-by":"crossref","unstructured":"Lee JR, Wang L, Wong A (2021) Emotionnet nano: An efficient deep convolutional neural network design for real-time facial expression recognition. Front Artif Intell, p 105","DOI":"10.3389\/frai.2020.609673"},{"key":"19012_CR47","doi-asserted-by":"crossref","unstructured":"Zou W, Zhang D, Lee DJ (2021) A new multi-feature fusion based convolutional neural network for facial expression recognition. Appl Intell, pp 1\u201312","DOI":"10.1007\/s10489-021-02575-0"},{"issue":"4","key":"19012_CR48","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1007\/s11554-021-01123-w","volume":"18","author":"L Wang","year":"2021","unstructured":"Wang L, He Z, Meng B, Liu K, Dou Q, Yang X (2021) Two-pathway attention network for real-time facial expression recognition. J Real-Time Image Proc 18(4):1173\u20131182","journal-title":"J Real-Time Image Proc"},{"key":"19012_CR49","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.patrec.2022.01.013","volume":"155","author":"D Gera","year":"2022","unstructured":"Gera D, Balasubramanian S, Jami A (2022) Cern: Compact facial expression recognition net. Pattern Recogn Lett 155:9\u201318","journal-title":"Pattern Recogn Lett"},{"issue":"11","key":"19012_CR50","doi-asserted-by":"crossref","first-page":"2884","DOI":"10.1109\/TIFS.2018.2833032","volume":"13","author":"X Wu","year":"2018","unstructured":"Wu X, He R, Sun Z, Tan T (2018) A light cnn for deep face representation with noisy labels. IEEE Trans Inf Forensics Secur 13(11):2884\u20132896","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"19012_CR51","doi-asserted-by":"crossref","unstructured":"Li M, Li X, Sun W, Wang X, Wang S (2021) Efficient convolutional neural network with multi-kernel enhancement features for real-time facial expression recognition. J Real-Time Image Process, pp 1\u201312","DOI":"10.1007\/s11554-021-01088-w"},{"key":"19012_CR52","doi-asserted-by":"crossref","unstructured":"Saurav S, Saini R, Singh S (2021) Emnet: a deep integrated convolutional neural network for facial emotion recognition in the wild. Appl Intell, pp 1\u201328","DOI":"10.1007\/s10489-020-02125-0"},{"key":"19012_CR53","doi-asserted-by":"crossref","first-page":"104367","DOI":"10.1109\/ACCESS.2021.3099075","volume":"9","author":"J Kim","year":"2021","unstructured":"Kim J, Kang JK, Kim Y (2021) A resource efficient integer-arithmetic-only fpga-based cnn accelerator for real-time facial emotion recognition. IEEE Access 9:104367\u2013104381","journal-title":"IEEE Access"},{"key":"19012_CR54","doi-asserted-by":"crossref","first-page":"101172","DOI":"10.1109\/ACCESS.2021.3095844","volume":"9","author":"N El Zarif","year":"2021","unstructured":"El Zarif N, Montazeri L, Leduc-Primeau F, Sawan M (2021) Mobile-optimized facial expression recognition techniques. IEEE Access 9:101172\u2013101185","journal-title":"IEEE Access"},{"key":"19012_CR55","doi-asserted-by":"crossref","first-page":"1954","DOI":"10.1109\/LSP.2020.3031504","volume":"27","author":"P Jiang","year":"2020","unstructured":"Jiang P, Wan B, Wang Q, Wu J (2020) Fast and efficient facial expression recognition using a gabor convolutional network. IEEE Signal Process Lett 27:1954\u20131958","journal-title":"IEEE Signal Process Lett"},{"key":"19012_CR56","doi-asserted-by":"crossref","unstructured":"Zhao Z, Liu Q, Zhou F (2021) Robust lightweight facial expression recognition network with label distribution training. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 3510\u20133519","DOI":"10.1609\/aaai.v35i4.16465"},{"key":"19012_CR57","doi-asserted-by":"crossref","unstructured":"Xia Y, Yu H, Wang X, Jian M, Wang FY (2021) Relation-aware facial expression recognition. IEEE Trans Cogn Dev Syst","DOI":"10.1109\/TCDS.2021.3100131"},{"key":"19012_CR58","doi-asserted-by":"crossref","unstructured":"Wang K, Peng X, Yang J, Lu S, Qiao Y (2020) Suppressing uncertainties for large-scale facial expression recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6897\u20136906","DOI":"10.1109\/CVPR42600.2020.00693"},{"issue":"9","key":"19012_CR59","doi-asserted-by":"crossref","first-page":"3046","DOI":"10.3390\/s21093046","volume":"21","author":"S Minaee","year":"2021","unstructured":"Minaee S, Minaei M, Abdolrashidi A (2021) Deep-emotion: facial expression recognition using attentional convolutional network. Sensors 21(9):3046","journal-title":"Sensors"},{"key":"19012_CR60","doi-asserted-by":"crossref","unstructured":"Xia Hy, Li C, Tan Y, Li L, Song S (2021) Destruction and reconstruction learning for facial expression recognition. IEEE MultiMedia","DOI":"10.1109\/MMUL.2021.3076834"},{"key":"19012_CR61","doi-asserted-by":"crossref","first-page":"6544","DOI":"10.1109\/TIP.2021.3093397","volume":"30","author":"Z Zhao","year":"2021","unstructured":"Zhao Z, Liu Q, Wang S (2021) Learning deep global multi-scale and local attention features for facial expression recognition in the wild. IEEE Trans Image Process 30:6544\u20136556","journal-title":"IEEE Trans Image Process"},{"key":"19012_CR62","doi-asserted-by":"crossref","unstructured":"Ding H, Zhou P, Chellappa R (2020) Occlusion-adaptive deep network for robust facial expression recognition. In: 2020 IEEE International joint conference on biometrics (IJCB). IEEE, pp 1\u20139","DOI":"10.1109\/IJCB48548.2020.9304923"},{"key":"19012_CR63","doi-asserted-by":"crossref","unstructured":"Indolia S, Nigam S, Singh R (2023) A framework for facial expression recognition using deep self-attention network. J Ambient Intell Humaniz Comput, pp 1\u201320","DOI":"10.1007\/s12652-023-04627-4"},{"issue":"2","key":"19012_CR64","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1007\/s00371-019-01627-4","volume":"36","author":"K Li","year":"2020","unstructured":"Li K, Jin Y, Akram MW, Han R, Chen J (2020) Facial expression recognition with convolutional neural networks via a new face cropping and rotation strategy. Vis Comput 36(2):391\u2013404","journal-title":"Vis Comput"},{"key":"19012_CR65","doi-asserted-by":"crossref","unstructured":"Ma Y, Wang X, Wei L (2021) Multi-level spatial and semantic enhancement network for expression recognition. Appl Intell, pp 1\u201314","DOI":"10.1007\/s10489-021-02254-0"},{"issue":"8","key":"19012_CR66","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.1007\/s00371-019-01759-7","volume":"36","author":"X Liu","year":"2020","unstructured":"Liu X, Zhou F (2020) Improved curriculum learning using ssm for facial expression recognition. Vis Comput 36(8):1635\u20131649","journal-title":"Vis Comput"},{"issue":"6","key":"19012_CR67","doi-asserted-by":"crossref","first-page":"2250","DOI":"10.3390\/s21062250","volume":"21","author":"L Liu","year":"2021","unstructured":"Liu L, Jiang R, Huo J, Chen J (2021) Self-difference convolutional neural network for facial expression recognition. Sensors 21(6):2250","journal-title":"Sensors"},{"key":"19012_CR68","doi-asserted-by":"crossref","unstructured":"Sun M, Cui W, Zhang Y, Yu S, Liao X, Hu B, Li Y (2023) Attention-rectified and texture-enhanced cross-attention transformer feature fusion network for facial expression recognition. IEEE Trans Ind Inform","DOI":"10.1109\/TII.2023.3253188"},{"key":"19012_CR69","doi-asserted-by":"crossref","unstructured":"Wu F, Pang C, Zhang B (2021) Facecaps for facial expression recognition. Computer Animation and Virtual Worlds, p e2021","DOI":"10.1002\/cav.2021"},{"issue":"4","key":"19012_CR70","doi-asserted-by":"crossref","first-page":"2269","DOI":"10.1007\/s10489-020-01895-x","volume":"51","author":"D Li","year":"2021","unstructured":"Li D, Zhao X, Yuan G, Liu Y, Liu G (2021) Robustness comparison between the capsule network and the convolutional network for facial expression recognition. Appl Intell 51(4):2269\u20132278","journal-title":"Appl Intell"},{"key":"19012_CR71","volume":"189","author":"Q Zhu","year":"2022","unstructured":"Zhu Q, Mao Q, Jia H, Noi OEN, Tu J (2022) Convolutional relation network for facial expression recognition in the wild with few-shot learning. Expert Syst Appl 189:116046","journal-title":"Expert Syst Appl"},{"key":"19012_CR72","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhanga F, Xu C (2021) Joint expression synthesis and representation learning for facial expression recognition. IEEE Trans Circ Syst Video Tech","DOI":"10.1109\/TCSVT.2021.3056098"},{"key":"19012_CR73","unstructured":"Ma F, Sun B, Li S (2021) Facial expression recognition with visual transformers and attentional selective fusion. IEEE Trans Affect Comput"},{"key":"19012_CR74","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1109\/TIP.2020.3037467","volume":"30","author":"Y Tang","year":"2020","unstructured":"Tang Y, Zhang X, Hu X, Wang S, Wang H (2020) Facial expression recognition using frequency neural network. IEEE Trans Image Process 30:444\u2013457","journal-title":"IEEE Trans Image Process"},{"key":"19012_CR75","doi-asserted-by":"crossref","unstructured":"Huang M, Zhang X, Lan X, Wang H, Tang Y (2021) Convolution by multiplication: accelerated two-stream fourier domain convolutional neural network for facial expression recognition. IEEE Trans Circ Syst Video Tech","DOI":"10.1109\/TCSVT.2021.3073558"},{"key":"19012_CR76","doi-asserted-by":"crossref","unstructured":"Zhou Y, Jin L, Ma G, Xu X (2021) Quaternion capsule neural network with region attention for facial expression recognition in color images. IEEE Trans Emerg Top Comput Intell","DOI":"10.1109\/TETCI.2021.3120513"},{"key":"19012_CR77","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.neucom.2021.07.017","volume":"462","author":"Y Liu","year":"2021","unstructured":"Liu Y, Zhang X, Zhou J, Fu L (2021) Sg-dsn: a semantic graph-based dual-stream network for facial expression recognition. Neurocomputing 462:320\u2013330","journal-title":"Neurocomputing"},{"key":"19012_CR78","doi-asserted-by":"crossref","unstructured":"Huang W, Zhang S, Zhang P, Zha Y, Fang Y, Zhang Y (2021) Identity-aware facial expression recognition via deep metric learning based on synthesized images. IEEE Trans Multimedia","DOI":"10.1109\/TMM.2021.3096068"},{"key":"19012_CR79","doi-asserted-by":"crossref","unstructured":"Farzaneh AH, Qi X (2021) Facial expression recognition in the wild via deep attentive center loss. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 2402\u20132411","DOI":"10.1109\/WACV48630.2021.00245"},{"key":"19012_CR80","doi-asserted-by":"crossref","unstructured":"Xie W, Wu H, Tian Y, Bai M, Shen L (2021) Triplet loss with multistage outlier suppression and class-pair margins for facial expression recognition. IEEE Trans Circ Syst Video Tech","DOI":"10.1109\/TCSVT.2021.3063052"},{"key":"19012_CR81","doi-asserted-by":"crossref","first-page":"2016","DOI":"10.1109\/TIP.2021.3049955","volume":"30","author":"H Li","year":"2021","unstructured":"Li H, Wang N, Ding X, Yang X, Gao X (2021) Adaptively learning facial expression representation via cf labels and distillation. IEEE Trans Image Process 30:2016\u20132028","journal-title":"IEEE Trans Image Process"},{"key":"19012_CR82","doi-asserted-by":"crossref","unstructured":"Verma K, Khunteta A (2017) Facial expression recognition using gabor filter and multi-layer artificial neural network. In: 2017 International conference on information, communication, instrumentation and control (ICICIC). IEEE, pp 1\u20135","DOI":"10.1109\/ICOMICON.2017.8279123"},{"issue":"3","key":"19012_CR83","doi-asserted-by":"crossref","first-page":"2541","DOI":"10.1007\/s00521-022-07742-z","volume":"35","author":"S Saurav","year":"2023","unstructured":"Saurav S, Sharma A, Saini R, Singh S (2023) An attention-guided convolutional neural network for automated classification of brain tumor from mri. Neural Comput Appl 35(3):2541\u20132560","journal-title":"Neural Comput Appl"},{"key":"19012_CR84","unstructured":"Van\u00a0der Maaten L, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9(11)"},{"key":"19012_CR85","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp 618\u2013626","DOI":"10.1109\/ICCV.2017.74"},{"key":"19012_CR86","unstructured":"Carrier PL, Courville A, Goodfellow IJ, Mirza M, Bengio Y (2013) Fer-2013 face database. Universit de Montral"},{"issue":"1","key":"19012_CR87","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1109\/TIP.2018.2868382","volume":"28","author":"S Li","year":"2018","unstructured":"Li S, Deng W (2018) Reliable crowdsourcing and deep locality-preserving learning for unconstrained facial expression recognition. IEEE Trans Image Process 28(1):356\u2013370","journal-title":"IEEE Trans Image Process"},{"key":"19012_CR88","doi-asserted-by":"crossref","unstructured":"Dhall A, Goecke R, Lucey S, Gedeon T (2011) Static facial expression analysis in tough conditions: data, evaluation protocol and benchmark. In: 2011 IEEE International conference on computer vision workshops (ICCV Workshops). IEEE, pp 2106\u20132112","DOI":"10.1109\/ICCVW.2011.6130508"},{"issue":"2","key":"19012_CR89","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1007\/s00371-019-01630-9","volume":"36","author":"A Agrawal","year":"2020","unstructured":"Agrawal A, Mittal N (2020) Using cnn for facial expression recognition: a study of the effects of kernel size and number of filters on accuracy. Vis Comput 36(2):405\u2013412","journal-title":"Vis Comput"},{"issue":"11","key":"19012_CR90","doi-asserted-by":"crossref","first-page":"2914","DOI":"10.1109\/TMM.2020.2966858","volume":"22","author":"G Wen","year":"2020","unstructured":"Wen G, Chang T, Li H, Jiang L (2020) Dynamic objectives learning for facial expression recognition. IEEE Trans Multimedia 22(11):2914\u20132925","journal-title":"IEEE Trans Multimedia"},{"key":"19012_CR91","doi-asserted-by":"crossref","unstructured":"Liu P, Lin Y, Meng Z, Lu L, Deng W, Zhou JT, Yang Y (2021) Point adversarial self-mining: a simple method for facial expression recognition. IEEE Trans Cybern","DOI":"10.1109\/TCYB.2021.3085744"},{"key":"19012_CR92","unstructured":"Hayale W, Negi PS, Mahoor M (2021) Deep siamese neural networks for facial expression recognition in the wild. IEEE Trans Affect Comput"},{"key":"19012_CR93","doi-asserted-by":"crossref","first-page":"18876","DOI":"10.1109\/ACCESS.2021.3054332","volume":"9","author":"C Liu","year":"2021","unstructured":"Liu C, Hirota K, Ma J, Jia Z, Dai Y (2021) Facial expression recognition using hybrid features of pixel and geometry. IEEE Access 9:18876\u201318889","journal-title":"IEEE Access"},{"key":"19012_CR94","first-page":"116321","volume":"96","author":"KY Tsai","year":"2021","unstructured":"Tsai KY, Tsai YW, Lee YC, Ding JJ, Chang RY (2021) Frontalization and adaptive exponential ensemble rule for deep-learning-based facial expression recognition system. Signal Process: Image Commun 96:116321","journal-title":"Signal Process: Image Commun"},{"issue":"9","key":"19012_CR95","doi-asserted-by":"crossref","first-page":"6253","DOI":"10.1109\/TCSVT.2022.3165321","volume":"32","author":"H Liu","year":"2022","unstructured":"Liu H, Cai H, Lin Q, Li X, Xiao H (2022) Adaptive multilayer perceptual attention network for facial expression recognition. IEEE Trans Circuits Syst Video Technol 32(9):6253\u20136266","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"19012_CR96","doi-asserted-by":"crossref","first-page":"26756","DOI":"10.1109\/ACCESS.2022.3156598","volume":"10","author":"AP Fard","year":"2022","unstructured":"Fard AP, Mahoor MH (2022) Ad-corre: Adaptive correlation-based loss for facial expression recognition in the wild. IEEE Access 10:26756\u201326768","journal-title":"IEEE Access"},{"key":"19012_CR97","doi-asserted-by":"crossref","unstructured":"Li Y, Lu Y, Chen B, Zhang Z, Li J, Lu G, Zhang D (2021) Learning informative and discriminative features for facial expression recognition in the wild. IEEE Trans Circ Syst Video Tech","DOI":"10.1109\/TCSVT.2021.3103760"},{"key":"19012_CR98","unstructured":"Choi JY, Lee B (2021) Combining deep convolutional neural networks with stochastic ensemble weight optimization for facial expression recognition in the wild. IEEE Transactions on Multimedia"},{"key":"19012_CR99","unstructured":"Zhao R, Liu T, Huang Z, Lun DPK, Lam KK (2021) Geometry-aware facial expression recognition via attentive graph convolutional networks. IEEE Trans Affect Comput"},{"key":"19012_CR100","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2022.104399","volume":"120","author":"X Tong","year":"2022","unstructured":"Tong X, Sun S, Fu M (2022) Adaptive weight based on overlapping blocks network for facial expression recognition. Image Vis Comput 120:104399","journal-title":"Image Vis Comput"},{"key":"19012_CR101","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.patcog.2019.03.019","volume":"92","author":"S Xie","year":"2019","unstructured":"Xie S, Hu H, Wu Y (2019) Deep multi-path convolutional neural network joint with salient region attention for facial expression recognition. Pattern Recogn 92:177\u2013191","journal-title":"Pattern Recogn"},{"key":"19012_CR102","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.neucom.2018.03.034","volume":"296","author":"W Sun","year":"2018","unstructured":"Sun W, Zhao H, Jin Z (2018) A visual attention based roi detection method for facial expression recognition. Neurocomputing 296:12\u201322","journal-title":"Neurocomputing"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19012-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19012-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19012-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T05:03:45Z","timestamp":1746075825000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19012-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,2]]},"references-count":102,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["19012"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19012-2","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,2]]},"assertion":[{"value":"30 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 June 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}