{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T00:21:01Z","timestamp":1767140461681,"version":"build-2238731810"},"reference-count":86,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2019,6,14]],"date-time":"2019-06-14T00:00:00Z","timestamp":1560470400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,6,14]],"date-time":"2019-06-14T00:00:00Z","timestamp":1560470400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"China National Science Foundation","award":["60973083"],"award-info":[{"award-number":["60973083"]}]},{"name":"China National Science Foundation","award":["61273363"],"award-info":[{"award-number":["61273363"]}]},{"DOI":"10.13039\/501100012245","name":"Science and Technology Planning Project of Guangdong Province","doi-asserted-by":"crossref","award":["2014A010103009"],"award-info":[{"award-number":["2014A010103009"]}],"id":[{"id":"10.13039\/501100012245","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012245","name":"Science and Technology Planning Project of Guangdong Province","doi-asserted-by":"crossref","award":["2015A020217002"],"award-info":[{"award-number":["2015A020217002"]}],"id":[{"id":"10.13039\/501100012245","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100010256","name":"Guangzhou Municipal Science and Technology Project","doi-asserted-by":"publisher","award":["201504291154480"],"award-info":[{"award-number":["201504291154480"]}],"id":[{"id":"10.13039\/501100010256","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010256","name":"Guangzhou Municipal Science and Technology Project","doi-asserted-by":"publisher","award":["201803010088"],"award-info":[{"award-number":["201803010088"]}],"id":[{"id":"10.13039\/501100010256","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1007\/s10489-019-01491-8","type":"journal-article","created":{"date-parts":[[2019,6,14]],"date-time":"2019-06-14T01:11:12Z","timestamp":1560474672000},"page":"4319-4334","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Facial expression recognition sensing the complexity of testing samples"],"prefix":"10.1007","volume":"49","author":[{"given":"Tianyuan","family":"Chang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0463-8178","authenticated-orcid":false,"given":"Huihui","family":"Li","sequence":"additional","affiliation":[]},{"given":"Guihua","family":"Wen","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Jiajiong","family":"Ma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,6,14]]},"reference":[{"issue":"2016","key":"1491_CR1","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.neucom.2016.12.043","volume":"230","author":"Y Sun","year":"2017","unstructured":"Sun Y, Wen G (2017) Cognitive facial expression recognition with constrained dimensionality reduction. Neurocomputing 230(2016):397\u2013408","journal-title":"Neurocomputing"},{"key":"1491_CR2","unstructured":"Friesen WV, Ekman P (1983) EMFACS-7: Emotional Facial Action Coding System"},{"issue":"9","key":"1491_CR3","doi-asserted-by":"crossref","first-page":"2912","DOI":"10.1007\/s10489-017-1121-y","volume":"48","author":"MH Siddiqi","year":"2018","unstructured":"Siddiqi MH (2018) Accurate and robust facial expression recognition system using real-time YouTube-based datasets[J]. Appl Intell 48(9):2912\u20132929","journal-title":"Appl Intell"},{"key":"1491_CR4","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1016\/j.patcog.2016.07.026","volume":"61","author":"AT Lopes","year":"2017","unstructured":"Lopes AT, de Aguiar E, De Souza AF, Oliveira-Santos T (2017) Facial expression recognition with convolutional neural networks: coping with few data and the training sample order. Pattern Recogn 61:610\u2013628","journal-title":"Pattern Recogn"},{"key":"1491_CR5","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.neucom.2013.02.033","volume":"118","author":"G Wen","year":"2013","unstructured":"Wen G, Wei J, Wang J, Zhou T, Chen L (2013) Cognitive gravitation model for classification on small noisy data. Neurocomputing 118:245\u2013252","journal-title":"Neurocomputing"},{"key":"1491_CR6","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.newideapsych.2014.06.005","volume":"26","author":"G Baruchello","year":"2015","unstructured":"Baruchello G (2015) A classification of classic, gestalt psychology and the tropes of rthetoric. New idea Pscychol 26:10\u201324","journal-title":"New idea Pscychol"},{"key":"1491_CR7","doi-asserted-by":"crossref","first-page":"7225","DOI":"10.1007\/s10994-013-5422-z","volume":"95","author":"MR Smith","year":"2014","unstructured":"Smith MR, Martinez T, Giraud-Carrier C (2014) An instance level analysis of data complexity. Mach Learn 95:7225\u20137256","journal-title":"Mach Learn"},{"key":"1491_CR8","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.patcog.2017.10.038","volume":"76","author":"AL Brun","year":"2018","unstructured":"Brun AL, Britto AS Jr, Oliveira LS, Enembreck F, Sabourin R (2018) A framework for dynamic classifier selection oriented by the classification problem difficulty[J]. Pattern Recogn 76:175\u2013190","journal-title":"Pattern Recogn"},{"key":"1491_CR9","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1016\/j.neucom.2015.09.083","volume":"174","author":"Z Wang","year":"2016","unstructured":"Wang Z, Ruan Q, An G (2016) Facial expression recognition using sparse local fisher discriminant analysis. Neurocomputing 174:756\u2013766","journal-title":"Neurocomputing"},{"key":"1491_CR10","doi-asserted-by":"crossref","unstructured":"Savran A, Cao H, Nenkova A, Verma R (2015) Temporal Bayesian Fusion for Affect Sensing: Combining Video, Audio, and Lexical Modalities. IEEE Trans. Cybern","DOI":"10.1109\/TCYB.2014.2362101"},{"key":"1491_CR11","unstructured":"Zhang W, Shan S, Gao W, Chen X, Zhang H (2005) Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A novel non-statistical model for face representation and recognition,\u201d in Proceedings of the IEEE International Conference on Computer Vision"},{"key":"1491_CR12","doi-asserted-by":"crossref","unstructured":"Dahmane M, Meunier J (2011) Emotion recognition using dynamic grid-based HoG features. in 2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011","DOI":"10.1109\/FG.2011.5771368"},{"key":"1491_CR13","doi-asserted-by":"crossref","unstructured":"Berretti S, Ben Amor B, Daoudi M, Del Bimbo A (2011) 3D facial expression recognition using SIFT descriptors of automatically detected keypoints. Vis Comput","DOI":"10.1007\/s00371-011-0611-x"},{"issue":"6","key":"1491_CR14","first-page":"449","volume":"8","author":"MA Jaffar","year":"2017","unstructured":"Jaffar MA (2017) Facial expression recognition using hybrid texture features based ensemble classifier. Int J Adv Comput Sci Appl 8(6):449\u2013453","journal-title":"Int J Adv Comput Sci Appl"},{"issue":"6","key":"1491_CR15","doi-asserted-by":"crossref","first-page":"7803","DOI":"10.1007\/s11042-016-3418-y","volume":"76","author":"D Ghimire","year":"2017","unstructured":"Ghimire D, Jeong S, Lee J, Park SH (2017) Facial expression recognition based on local region specific features and support vector machines. Multimed Tools Appl 76(6):7803\u20137821","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"1491_CR16","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s11760-010-0177-5","volume":"6","author":"SM Lajevardi","year":"2012","unstructured":"Lajevardi SM, Hussain ZM (2012) Automatic facial expression recognition: feature extraction and selection. Signal, Image Video Proc 6(1):159\u2013169","journal-title":"Signal, Image Video Proc"},{"key":"1491_CR17","doi-asserted-by":"crossref","unstructured":"Shan C, Gritti T (2008) Learning discriminative LBP-histogram bins for facial expression recognition. Proc Br Mach Vis Conf:27.1\u201327.10","DOI":"10.5244\/C.22.27"},{"issue":"c","key":"1491_CR18","first-page":"1","volume":"3045","author":"S Khan","year":"2017","unstructured":"Khan S, Chen L, Yan H (2017) Co-clustering to reveal salient facial features for expression recognition. IEEE Trans Affect Comput 3045(c):1\u201314","journal-title":"IEEE Trans Affect Comput"},{"issue":"2","key":"1491_CR19","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/TSMCB.2005.859075","volume":"36","author":"M Pantic","year":"2006","unstructured":"Pantic M, Patras I (2006) Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences. IEEE Trans Syst Man, Cybern Part B Cybern 36(2):433\u2013449","journal-title":"IEEE Trans Syst Man, Cybern Part B Cybern"},{"issue":"10","key":"1491_CR20","doi-asserted-by":"crossref","first-page":"1683","DOI":"10.1109\/TPAMI.2007.1094","volume":"29","author":"Y Tong","year":"2007","unstructured":"Tong Y, Liao W, Ji Q (2007) Facial action unit recognition by exploiting their dynamic and semantic relationships. IEEE Trans Pattern Anal Mach Intell 29(10):1683\u20131699","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"1491_CR21","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.neucom.2015.02.011","volume":"159","author":"M Liu","year":"2015","unstructured":"Liu M, Li S, Shan S, Chen X (2015) AU-inspired deep networks for facial expression feature learning. Neurocomputing 159(1):126\u2013136","journal-title":"Neurocomputing"},{"key":"1491_CR22","first-page":"2857","volume":"2011","author":"M Ranzato","year":"2011","unstructured":"Ranzato M, Susskind J, Mnih V, Hinton G (2011) On deep generative models with applications to recognition. Cvpr 2011:2857\u20132864","journal-title":"Cvpr"},{"key":"1491_CR23","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Li F-F (2009) ImageNet: a large-scale hierarchical image database. Cvpr:248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1491_CR24","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Maaten LVD, Weinberger KQ (2017) Densely connected convolutional networks. 2017 IEEE Conf Comput Vis Pattern Recognit:2261\u20132269","DOI":"10.1109\/CVPR.2017.243"},{"key":"1491_CR25","doi-asserted-by":"crossref","unstructured":"Li J, Lam EY (2015) Facial expression recognition using deep neural networks. Imaging Syst Tech (IST), 2015 IEEE Int Conf:1\u20136","DOI":"10.1109\/IST.2015.7294547"},{"key":"1491_CR26","doi-asserted-by":"crossref","unstructured":"Yu Z, Zhang C (2015) Image based static facial expression recognition with multiple deep network learning. Proc 2015 ACM Int Conf Multimodal Interact - ICMI \u201815:435\u2013442","DOI":"10.1145\/2818346.2830595"},{"key":"1491_CR27","unstructured":"Tang Y (2013) Deep learning using linear support vector machines. Comput Therm Sci"},{"key":"1491_CR28","unstructured":"Xu M, Cheng W, Zhao Q, Ma L, Xu F (2015) Facial expression recognition based on transfer learning from deep convolutional networks. 2015 11th Int Conf Nat Comput:702\u2013708"},{"key":"1491_CR29","unstructured":"Ng H-W, Nguyen VD, Vonikakis V, Winkler S (2015) Deep learning for emotion recognition on small datasets using transfer learning. Proc 2015 ACM Int Conf Multimodal Interact - ICMI \u201815:443\u2013449"},{"key":"1491_CR30","doi-asserted-by":"crossref","unstructured":"Li D, Wen G (2017) MRMR-based ensemble pruning for facial expression recognition. Multimed Tools Appl","DOI":"10.1007\/s11042-017-5105-z"},{"key":"1491_CR31","unstructured":"Li D, Wen G, Hou Z, Huan E, Hu Y, Li H (2018) RTCRelief-F: an effective clustering and ordering-based ensemble pruning algorithm for facial expression recognition. Knowl Inf Syst:1\u201332"},{"key":"1491_CR32","doi-asserted-by":"crossref","unstructured":"Ding H, Zhou SK, Chellappa R (2017) \u201cFaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition,\u201d Proc. - 12th IEEE Int. Conf. Autom. Face Gesture Recognition, FG 2017 - 1st Int. Work. Adapt. Shot Learn. Gesture Underst. Prod. ASL4GUP 2017, Biometrics Wild, Bwild 2017, Heteroge, pp. 118\u2013126","DOI":"10.1109\/FG.2017.23"},{"key":"1491_CR33","unstructured":"Al-Shabi M, Cheah WP, Connie T (2016) Facial Expression Recognition Using a Hybrid CNN\u2013 SIFT Aggregator. Int Work Multi-disciplinary Trends Artif Intell"},{"issue":"12","key":"1491_CR34","doi-asserted-by":"crossref","first-page":"2528","DOI":"10.1109\/TMM.2016.2598092","volume":"18","author":"T Zhang","year":"2016","unstructured":"Zhang T, Zheng W, Cui Z, Zong Y, Yan J (2016) A deep neural network-driven feature learning method for multi-view facial expression recognition [J]. IEEE Trans Multimed 18(12):2528\u20132536","journal-title":"IEEE Trans Multimed"},{"key":"1491_CR35","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1016\/j.patcog.2016.07.026","volume":"61","author":"AT Lopes","year":"2017","unstructured":"Lopes AT, Aguiar ED, Souza AF, Oliveira-Santos T (2017) Facial expression recognition with convolutional neural networks: coping with few data and the training sample order [J]. Pattern Recogn 61:610\u2013628","journal-title":"Pattern Recogn"},{"key":"1491_CR36","doi-asserted-by":"publisher","unstructured":"Chen J, Xu R, Liu L (2018) Deep peak-neutral difference feature for facial expression recognition[J]. Multimed Tools Appl. \nhttps:\/\/doi.org\/10.1007\/s11042-018-5909-5","DOI":"10.1007\/s11042-018-5909-5"},{"key":"1491_CR37","first-page":"603","volume":"28","author":"T Fang","year":"2011","unstructured":"Fang T, Zhao X, Ocegueda O, Shah SK, Kakadiaris IA (2011) 3D facial expression recognition: a perspective on promises and challenges[C]. IEEE Int Conf Autom Face Gesture Recog 28:603\u2013610","journal-title":"IEEE Int Conf Autom Face Gesture Recog"},{"issue":"7","key":"1491_CR38","doi-asserted-by":"crossref","first-page":"1438","DOI":"10.1109\/TMM.2016.2557063","volume":"18","author":"Q Zhen","year":"2016","unstructured":"Zhen Q, Huang D, Wang Y, Chen L (2016) Muscular movement model-based automatic 3D\/4D facial expression recognition[J]. IEEE Trans Multimed 18(7):1438\u20131450","journal-title":"IEEE Trans Multimed"},{"key":"1491_CR39","first-page":"1","volume":"99","author":"A Dapogny","year":"2017","unstructured":"Dapogny A, Bailly K, Dubuisson S (2017) Dynamic pose-robust facial expression recognition by multi-view pairwise conditional random forests [J]. IEEE Trans on Affect Comput 99:1\u201314","journal-title":"IEEE Trans on Affect Comput"},{"key":"1491_CR40","unstructured":"Drira H, Ben Amor B, Daoudi M, Srivastava A, Berretti S (2012) 3D dynamic expression recognition based on a novel deformation vector field and random Forest[C]. IEEE Int Conf Patt Recog:1104\u20131107"},{"issue":"12","key":"1491_CR41","doi-asserted-by":"crossref","first-page":"2443","DOI":"10.1109\/TCYB.2014.2308091","volume":"44","author":"B Ben Amor","year":"2017","unstructured":"Ben Amor B, Drira H, Berretti S, Daoudi M, Srivastava A (2017) 4D facial expression recognition by learning geometric deformations[J]. IEEE Trans Cybernet 44(12):2443\u20132457","journal-title":"IEEE Trans Cybernet"},{"key":"1491_CR42","doi-asserted-by":"crossref","unstructured":"Yao Y, Huang D, Yang X, Wang Y, Chen L (2018) Texture and Geometry Scattering Representation based Facial Expression Recognition in 2D+3D Videos [J], ACM Transactions on Multimedia Computing and Applications","DOI":"10.1145\/3131345"},{"issue":"8","key":"1491_CR43","doi-asserted-by":"crossref","first-page":"1872","DOI":"10.1109\/TPAMI.2012.230","volume":"35","author":"B Joan","year":"2013","unstructured":"Joan B, Stephane M (2013) Invariant scattering Nonvolution networks[J]. IEEE Trans Pattern Anal Mach Intell 35(8):1872\u20131886","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1491_CR44","unstructured":"Yang X, Huang D, Wang Y, Chen L (2015) Automatic 3D Facial Expression Recognition using Geometric Scattering Representation[C]. IEEE International Conference on Automatic Face and Gesture Recognition"},{"key":"1491_CR45","doi-asserted-by":"crossref","unstructured":"Liu Y, Zeng J, Shan S, Zheng Z (2018) Multi-channel pose-aware convolution neural networks for multi-view facial expression recognition[C], 13th IEEE International Conference on Automatic Face & Gesture Recognition","DOI":"10.1109\/FG.2018.00074"},{"key":"1491_CR46","doi-asserted-by":"crossref","unstructured":"Li W, Huang D, Li H, Wang Y (2018) Automatic 4D Facial Expression Recognition using Dynamic Geometrical Image Network[C], 13th IEEE International Conference on Automatic Face & Gesture Recognition","DOI":"10.1109\/FG.2018.00014"},{"key":"1491_CR47","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.knosys.2015.05.015","volume":"85","author":"I Mendialdua","year":"2015","unstructured":"Mendialdua I, Mart\u00ednez-Otzeta JM, Rodriguez-Rodriguez I, Ruiz-Vazquez T, Sierra B (2015) Dynamic selection of the best base classifier in one versus one[J]. Knowl-Based Syst 85:298\u2013310","journal-title":"Knowl-Based Syst"},{"issue":"11","key":"1491_CR48","doi-asserted-by":"crossref","first-page":"2188","DOI":"10.1016\/j.patcog.2005.02.010","volume":"38","author":"L Didaci","year":"2005","unstructured":"Didaci L, Giacinto G, Roli F, Marcialis GL (2005) A study on the performances of dynamic classifier selection based on local accuracy estimation[J]. Pattern Recogn 38(11):2188\u20132191","journal-title":"Pattern Recogn"},{"key":"1491_CR49","doi-asserted-by":"crossref","first-page":"3668","DOI":"10.1016\/j.eswa.2011.09.059","volume":"39","author":"J Xiao","year":"2012","unstructured":"Xiao J, Xie L, He C, Jiang X (2012) Dynamic classifier ensemble model for customer classification with imbalanced class distribution[J]. Expert Syst Appl 39:3668\u20133675","journal-title":"Expert Syst Appl"},{"issue":"9","key":"1491_CR50","doi-asserted-by":"crossref","first-page":"3544","DOI":"10.1016\/j.patcog.2012.02.034","volume":"45","author":"PR Cavalin","year":"2012","unstructured":"Cavalin PR, Sabourin R, Suen CY (2012) Logid: an adaptive framework combining local and global incremental learning for dynamic selection of ensembles of HMMs[J]. Pattern Recogn 45(9):3544\u20133556","journal-title":"Pattern Recogn"},{"key":"1491_CR51","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.engappai.2008.04.009","volume":"22","author":"G Szepannek","year":"2009","unstructured":"Szepannek G, Bischl B, Weihs C (2009) On the combination of locally optimal pairwise classifiers [J]. Eng Appl Artif Intell 22:79\u201385","journal-title":"Eng Appl Artif Intell"},{"key":"1491_CR52","doi-asserted-by":"crossref","unstructured":"de Souza BF, de Carvalho A, Calvo R, Ishii RP (2006) Multiclass svm model selection using particle swarm optimization[C]. Sixth Int Conf Hybrid Intel Syst, IEEE:31","DOI":"10.1109\/HIS.2006.264914"},{"key":"1491_CR53","doi-asserted-by":"crossref","first-page":"1925","DOI":"10.1016\/j.patcog.2014.12.003","volume":"48","author":"MO Rafael","year":"2015","unstructured":"Rafael MO (2015) Cruz, Robert Sabourin, George D.C. Cavalcanti, Tsang Ing Ren, META-DES: a dynamic ensemble selection framework using META-learning [J]. Pattern Recogn 48:1925\u20131935","journal-title":"Pattern Recogn"},{"issue":"2","key":"1491_CR54","doi-asserted-by":"crossref","first-page":"455","DOI":"10.12785\/amis\/070205","volume":"7","author":"C Xu","year":"2013","unstructured":"Xu C, Du PF, Feng ZY, Meng ZP, Cao TY, Dong CC (2013) Multi-modal emotion recognition fusing video and audio [J]. Appl Math Inform Sci 7(2):455\u2013462","journal-title":"Appl Math Inform Sci"},{"issue":"1","key":"1491_CR55","first-page":"83","volume":"11","author":"Y Wang","year":"2013","unstructured":"Wang Y, Yang X, Zou J (2013) Research of emotion recognition based on speech and facial expression[J]. Inst Adv Eng Sci 11(1):83\u201390","journal-title":"Inst Adv Eng Sci"},{"issue":"2","key":"1491_CR56","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1007\/s11704-014-2345-1","volume":"8","author":"SF Wang","year":"2014","unstructured":"Wang SF, He S, Wu Y, He MH, Ji Q (2014) Fusion of visible and thermal images for facial expression recognition [J]. Front Comput Sci 8(2):232\u2013242","journal-title":"Front Comput Sci"},{"issue":"1","key":"1491_CR57","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1109\/TCYB.2016.2625419","volume":"48","author":"A Majumder","year":"2018","unstructured":"Majumder A, Behera L, Subramanian VK (2018) Automatic facial expression recognition system using deep network-based data fusion [J]. IEEE Trans Cybernet 48(1):103\u2013114","journal-title":"IEEE Trans Cybernet"},{"key":"1491_CR58","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/978-3-319-51814-5_18","volume":"10133","author":"YC Sun","year":"2017","unstructured":"Sun YC, Yu J (2017) Facial expression recognition by fusing Gabor and local binary pattern features [J]. Multimed Model 10133:209\u2013220","journal-title":"Multimed Model"},{"issue":"5","key":"1491_CR59","doi-asserted-by":"crossref","first-page":"7365","DOI":"10.1007\/s11042-016-3419-x","volume":"76","author":"WC Wang","year":"2017","unstructured":"Wang WC, Chang FL, Liu YL, Wu XJ (2017) Expression recognition method based on evidence theory and local texture [J]. Multimed Tools Appl 76(5):7365\u20137379","journal-title":"Multimed Tools Appl"},{"key":"1491_CR60","doi-asserted-by":"crossref","unstructured":"Sun B, Li LD, Zhou GY et al (2016) Facial expression recognition in the wild based on multimodal texture features [J]. J Electron Imaging 25(6)","DOI":"10.1117\/1.JEI.25.6.061407"},{"issue":"5","key":"1491_CR61","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s12559-017-9472-6","volume":"9","author":"GH Wen","year":"2017","unstructured":"Wen GH, Hou Z, Li HH, Li DY, Jiang LJ, Xun EY (2017) Ensemble of deep neural networks with probability-based fusion for facial expression recognition [J]. Cogn Comput 9(5):597\u2013610","journal-title":"Cogn Comput"},{"key":"1491_CR62","doi-asserted-by":"crossref","unstructured":"Wen GH, Li HH, Li DY (2015) An ensemble convolutional echo state networks for facial expression recognition [C]. 2015 International Conference on Affective Computing and Intelligent Interaction (ACII), Xian, China 873\u2013878","DOI":"10.1109\/ACII.2015.7344677"},{"key":"1491_CR63","unstructured":"Li D, Wen G, Hou Z, Huan E, Hu Y, Li H (2018) RTCRelief-F: An effective clustering and ordering-based ensemble pruning algorithm for facial expression recognition[J]. Knowl Inf Syst:1\u201332"},{"key":"1491_CR64","first-page":"300","volume":"3","author":"M Sun","year":"2017","unstructured":"Sun M, Liu K, Hong Q (2017) An ECOC approach for microarray data classification based on minimizing feature related complexities. 10th Int Symp Comput Intell Des 3:300\u2013303","journal-title":"10th Int Symp Comput Intell Des"},{"issue":"6","key":"1491_CR65","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1109\/TPAMI.2006.116","volume":"28","author":"O Pujol","year":"2006","unstructured":"Pujol O, Radeva P, Vitri\u00e0 J (2006) Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes. IEEE Trans Pattern Anal Mach Intell 28(6):1007\u20131012","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1491_CR66","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/ICPR.2004.1334026","volume":"1","author":"EB Mansilla","year":"2004","unstructured":"Mansilla EB, Ho TK (2004) On classifier domains of competence. Proc - Int Conf Pattern Recognit 1:136\u2013139","journal-title":"Proc - Int Conf Pattern Recognit"},{"key":"1491_CR67","doi-asserted-by":"crossref","unstructured":"de Souto MCP, Lorena AC, Spolaor N, Costa IG (2010) Complexity measures of supervised classifications tasks: a case study for cancer gene expression data. Int Jt Conf Neural Networks:1\u20137","DOI":"10.1109\/IJCNN.2010.5596305"},{"key":"1491_CR68","doi-asserted-by":"crossref","unstructured":"Gui L, Baltrusaitis T, Morency L-P (2017) Curriculum learning for facial expression recognition. 12th IEEE Int Conf Autom Face Gesture Recognit:505\u2013511","DOI":"10.1109\/FG.2017.68"},{"key":"1491_CR69","unstructured":"Wu S, Zhong S, Liu Y (2017) Deep residual learning for image steganalysis. Multimed Tools Appl:1\u201317"},{"key":"1491_CR70","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.neunet.2014.09.005","volume":"64","author":"IJ Goodfellow","year":"2015","unstructured":"Goodfellow IJ et al (2015) Challenges in representation learning: a report on three machine learning contests. Neural Netw 64:59\u201363","journal-title":"Neural Netw"},{"issue":"12","key":"1491_CR71","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1109\/34.817413","volume":"21","author":"MJ Lyons","year":"1999","unstructured":"Lyons MJ (1999) Automatic classification of single facial images. IEEE Trans Pattern Anal Mach Intell 21(12):1357\u20131362","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1491_CR72","unstructured":"Lucey P, Cohn JF, Kanade T, Saragih J, Ambadar Z, Matthews I (2010) The extended cohn-kande dataset (CK+): a complete facial expression dataset for action unit and emotions pecified expression. Cvprw:94\u2013101"},{"key":"1491_CR73","doi-asserted-by":"crossref","unstructured":"Mollahosseini A, Chan D, Mahoor MH (2016) Going deeper in facial expression recognition using deep neural networks. IEEE Winter Conf Appl Comput Vis 1:\u201310","DOI":"10.1109\/WACV.2016.7477450"},{"key":"1491_CR74","unstructured":"Wu C, Wang S (2015) Multi-instance hidden Markov model for facial expression recognition. Int Conf Autom Face Gesture Recog"},{"key":"1491_CR75","doi-asserted-by":"crossref","unstructured":"Happy SL, Routray A (2015) Automatic facial expression recognition using features of salient facial patches. IEEE Trans Affect Comput","DOI":"10.1109\/TAFFC.2014.2386334"},{"key":"1491_CR76","first-page":"1339","volume":"75","author":"H Yan","year":"2018","unstructured":"Yan H (2018) Collaborative discriminative multi-metric learning for facial expression recognition in video. Pattern Recogn 75:1339\u20131351","journal-title":"Pattern Recogn"},{"key":"1491_CR77","first-page":"1","volume":"0","author":"A Barman","year":"2017","unstructured":"Barman A, Dutta P (2017) Facial expression recognition using distance and shape signature features. Pattern Recogn Lett 0:1\u20138","journal-title":"Pattern Recogn Lett"},{"key":"1491_CR78","doi-asserted-by":"crossref","unstructured":"Sariyanidi E, Gunes H, Cavallaro A (2017) \u201cLearning Bases of Activity for Facial Expression Recognition,\u201d IEEE Trans. Image Process","DOI":"10.1109\/TIP.2017.2662237"},{"key":"1491_CR79","doi-asserted-by":"crossref","unstructured":"Chen X, Yang X, Wang M, Zou J (2017) Convolution neural network for automatic facial expression recognition. Proc IEEE Int Conf Appl Syst Innov Appl Syst Innov Mod Technol ICASI 2017 814\u2013817","DOI":"10.1109\/ICASI.2017.7988558"},{"key":"1491_CR80","doi-asserted-by":"crossref","unstructured":"Liu M, Shan S, Wang R, Chen X (2014) Learning expressionlets on spatio-temporal manifold for dynamic facial expression recognition in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR.2014.226"},{"key":"1491_CR81","first-page":"1","volume":"10","author":"W Deaney","year":"2017","unstructured":"Deaney W, Venter I, Ghaziasgar M, Dodds R (2017) A comparison of facial feature representation methods for automatic facial expression recognition. Proc South African Inst Comput Sci Inf Technol 10:1\u201310","journal-title":"Proc South African Inst Comput Sci Inf Technol"},{"key":"1491_CR82","unstructured":"Guo Y, Tao D, Yu J, Hao X, Li Y, Tao D (2016) Deep Neural Networks with Relativity Learning for facial expression recognition,\u201d in 2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW"},{"key":"1491_CR83","doi-asserted-by":"crossref","unstructured":"Kim B-K, Roh J, Dong S-Y, Lee S-Y (2016) Hierarchical committee of deep convolutional neural networks for robust facial expression recognition. J Multimodal User Interfaces","DOI":"10.1007\/s12193-015-0209-0"},{"key":"1491_CR84","doi-asserted-by":"crossref","unstructured":"Wei W, Yang X-L, Zhou B et al (2012) Combined energy minimization for image reconstruction from few views. Math Probl Eng","DOI":"10.1155\/2012\/154630"},{"key":"1491_CR85","doi-asserted-by":"crossref","unstructured":"Wei W, Yang X-L, Shen P-Y et al (2012) Holes detection in anisotropic Sensornets: topological methods. Int J Distribut Sensor Netw","DOI":"10.1155\/2012\/135054"},{"issue":"5","key":"1491_CR86","doi-asserted-by":"crossref","first-page":"4794","DOI":"10.3390\/s110504794","volume":"11","author":"W Wei","year":"2011","unstructured":"Wei W, Qi Y (2011) Information potential fields navigation wireless adoc sensor networks. Sensors 11(5):4794\u20134807","journal-title":"Sensors"}],"updated-by":[{"DOI":"10.1007\/s10489-020-01709-0","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2020,7,27]],"date-time":"2020-07-27T00:00:00Z","timestamp":1595808000000}}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-019-01491-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10489-019-01491-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-019-01491-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T21:12:18Z","timestamp":1601068338000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10489-019-01491-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,14]]},"references-count":86,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["1491"],"URL":"https:\/\/doi.org\/10.1007\/s10489-019-01491-8","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,14]]},"assertion":[{"value":"14 June 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 July 2020","order":2,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":3,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The original version of this article unfortunately contained a mistake. Graphs c, d and e are missing in Figure 4. The correct and complete graphs of Figure 4 is shown here.","order":4,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}}]}}