{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T19:27:18Z","timestamp":1777663638454,"version":"3.51.4"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T00:00:00Z","timestamp":1604275200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T00:00:00Z","timestamp":1604275200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 61472330 and No. 61872301"],"award-info":[{"award-number":["No. 61472330 and No. 61872301"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s10489-020-01895-x","type":"journal-article","created":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T00:06:36Z","timestamp":1604361996000},"page":"2269-2278","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Robustness comparison between the capsule network and the convolutional network for facial expression recognition"],"prefix":"10.1007","volume":"51","author":[{"given":"Donghui","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingcong","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangjie","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangyuan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,2]]},"reference":[{"issue":"2","key":"1895_CR1","first-page":"126","volume":"47","author":"P Ekman","year":"1978","unstructured":"Ekman P, Friesen WV (1978) Facial Action Coding System (FACS): A technique for the measurement of facial action [J]. rivista di psichiatria 47(2):126\u2013138","journal-title":"rivista di psichiatria"},{"key":"1895_CR2","doi-asserted-by":"crossref","unstructured":"Mollahosseini A, Chan D, Mahoor MH (2016) Going deeper in facial expression recognition using deep neural networks. IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, pp 1\u201310","DOI":"10.1109\/WACV.2016.7477450"},{"key":"1895_CR3","doi-asserted-by":"crossref","unstructured":"Lucey P, Cohn JF, Kanade T, Saragih J, Ambadar Z, Matthews I (2010) The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, San Francisco, pp 94\u2013101","DOI":"10.1109\/CVPRW.2010.5543262"},{"key":"1895_CR4","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.jpdc.2019.04.017","volume":"131","author":"J Chen","year":"2019","unstructured":"Chen J, Lv Y, Xu R, Can X (2019) Automatic social signal analysis: facial expression recognition using difference convolution neural network [J]. J Parallel Distrib Comput 131:97\u2013102","journal-title":"J Parallel Distrib Comput"},{"key":"1895_CR5","unstructured":"Lopes AT, de Aguiar E, De Souza AF (2017) Thiago Oliveira-Santos. Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order [J]. Pattern Recogn 61"},{"issue":"04","key":"1895_CR6","doi-asserted-by":"publisher","first-page":"784","DOI":"10.21629\/JSEE.2017.04.18","volume":"28","author":"C Zhang","year":"2017","unstructured":"Zhang C, Wang P, Chen K, K\u00e4m\u00e4r\u00e4inen J-K (2017) Identity-aware convolutional neural networks for facial expression recognition. J Syst Eng Electron 28(04):784\u2013792","journal-title":"J Syst Eng Electron"},{"key":"1895_CR7","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.patrec.2019.12.013","volume":"131","author":"H Zhang","year":"2020","unstructured":"Zhang H, Huang B, Tian G (2020) Facial expression recognition based on deep convolution long short-term memory networks of double-channel weighted mixture. Pattern Recogn Lett 131:128\u2013134","journal-title":"Pattern Recogn Lett"},{"key":"1895_CR8","doi-asserted-by":"publisher","first-page":"4319","DOI":"10.1007\/s10489-019-01491-8","volume":"49","author":"T Chang","year":"2019","unstructured":"Chang T, Li H, Wen G, Hu Y, Ma J (2019) Facial expression recognition sensing the complexity of testing samples. Appl Intell 49:4319\u20134334. https:\/\/doi.org\/10.1007\/s10489-019-01491-8","journal-title":"Appl Intell"},{"key":"1895_CR9","first-page":"211","volume-title":"A Deep Structure for Facial Expression Recognition under Partial Occlusion","author":"Y Cheng","year":"2014","unstructured":"Cheng Y, Jiang B, Jia K (2014) A Deep Structure for Facial Expression Recognition under Partial Occlusion. 2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Kitakyushu, pp 211\u2013214"},{"key":"1895_CR10","doi-asserted-by":"crossref","unstructured":"Wang H, Gao J, Tong L, Yu L (2016) Facial expression recognition based on PHOG feature and sparse representation 2016 35th Chinese Control Conference (CCC), Chengdu, pp 3869\u20133874","DOI":"10.1109\/ChiCC.2016.7553957"},{"key":"1895_CR11","doi-asserted-by":"crossref","unstructured":"Xinli Yang, Ming Li, Shilin Zhao (2017) Facial expression recognition algorithm based on CNN and LBP feature fusion. ACM International Conference Proceeding Series, pp 33\u201338","DOI":"10.1145\/3175603.3175615"},{"key":"1895_CR12","doi-asserted-by":"publisher","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. Appl Intell 48:2912\u20132929. https:\/\/doi.org\/10.1007\/s10489-017-1121-y","journal-title":"Appl Intell"},{"key":"1895_CR13","unstructured":"Liu K, Hsu C, Wang W, Chiang H (2019) Real-Time Facial Expression Recognition Based on CNN 2019. International Conference on System Science and Engineering (ICSSE), Dong Hoi, pp 120\u2013123"},{"issue":"5","key":"1895_CR14","doi-asserted-by":"publisher","first-page":"2439","DOI":"10.1109\/TIP.2018.2886767","volume":"28","author":"Y Li","year":"2019","unstructured":"Li Y, Zeng J, Shan S, Chen X (2019) Occlusion aware facial expression recognition using CNN with attention mechanism. IEEE Trans Image Process 28(5):2439\u20132450","journal-title":"IEEE Trans Image Process"},{"key":"1895_CR15","doi-asserted-by":"publisher","first-page":"2659","DOI":"10.1007\/s10489-018-1388-7","volume":"49","author":"Z Wang","year":"2019","unstructured":"Wang Z, Zhang L, Wang B (2019) Sparse modified marginal fisher analysis for facial expression recognition. Appl Intell 49:2659\u20132671. https:\/\/doi.org\/10.1007\/s10489-018-1388-7","journal-title":"Appl Intell"},{"key":"1895_CR16","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. 2015 IEEE Conference on computer Vision and Pattern Recognition (CVPR), Boston, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"1895_CR17","unstructured":"Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. 31st Conference on Neural Information Processing Systems (NIPS 2017)"},{"issue":"11","key":"1895_CR18","doi-asserted-by":"publisher","first-page":"3407","DOI":"10.1016\/j.patcog.2015.04.025","volume":"48","author":"X Fan","year":"2015","unstructured":"Fan X, Tjahjadi T (2015) A spatial-temporal framework based on histogram of gradients and optical flow for facial expression recognition in video sequences. Pattern Recogn 48(11):3407\u20133416. https:\/\/doi.org\/10.1016\/j.patcog.2015.04.025","journal-title":"Pattern Recogn"},{"issue":"5","key":"1895_CR19","doi-asserted-by":"publisher","first-page":"1740","DOI":"10.1109\/TIP.2012.2235848","volume":"22","author":"A Ramirez Rivera","year":"2013","unstructured":"Ramirez Rivera A, Rojas Castillo J, Oksam Chae O (2013) Local directional number pattern for face analysis: face and expression recognition. IEEE Trans Image Process 22(5):1740\u20131752","journal-title":"IEEE Trans Image Process"},{"issue":"2","key":"1895_CR20","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1016\/j.eswa.2012.07.074","volume":"40","author":"THH Zavaschi","year":"2013","unstructured":"Zavaschi THH, Britto AS, Oliveira LES, Koerich AL (2013) Fusion of feature sets and classifiers for facial expression recognition. Expert Syst Appl 40(2):646\u2013655","journal-title":"Expert Syst Appl"},{"issue":"1","key":"1895_CR21","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.patcog.2011.05.006","volume":"45","author":"W Gu","year":"2012","unstructured":"Gu W, Xiang C, Venkatesh Y, Huang D, Lin H (2012) Facial expression recognition using radial encoding of local Gabor features and classifier synthesis. Pattern Recogn 45(1):80\u201391","journal-title":"Pattern Recogn"},{"key":"1895_CR22","doi-asserted-by":"crossref","unstructured":"F. De la Torre, W. Chu, X. Xiong, F. Vicente, X. Ding and J. Cohn. IntraFace. 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Ljubljana, 2015, pp. 1\u20138","DOI":"10.1109\/FG.2015.7163082"},{"key":"1895_CR23","doi-asserted-by":"crossref","unstructured":"Yang H, Ciftci U, Yin L (2018) Facial Expression Recognition by De-expression Residue Learning. 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake CityT, pp 2168\u20132177","DOI":"10.1109\/CVPR.2018.00231"},{"key":"1895_CR24","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1007\/s12559-019-09654-y","volume":"11","author":"X Sun","year":"2019","unstructured":"Sun X, Lv M (2019) Facial expression recognition based on a hybrid model combining deep and shallow features. Cogn Comput 11:587\u2013597. https:\/\/doi.org\/10.1007\/s12559-019-09654-y","journal-title":"Cogn Comput"},{"key":"1895_CR25","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1007\/s11263-017-1055-1","volume":"126","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Luo P, Chen CL, Tang X (2018) From facial expression recognition to interpersonal relation prediction. Int J Comput Vis 126:550\u2013569. https:\/\/doi.org\/10.1007\/s11263-017-1055-1","journal-title":"Int J Comput Vis"},{"key":"1895_CR26","unstructured":"Liu X, Kumar BVKV, You J, Jia P (2017) Adaptive Deep Metric Learning for Identity-Aware Facial Expression Recognition, vol 2017. IEEE Conference on Computer Vision and Pattern Recognition workshops (CVPRW), Honolulu, pp 522\u2013531"},{"key":"1895_CR27","first-page":"958","volume-title":"Best practices for convolutional neural networks applied to visual document analysis","author":"PY Simard","year":"2003","unstructured":"Simard PY, Steinkraus D, Platt JC (2003) Best practices for convolutional neural networks applied to visual document analysis. Seventh International Conference on Document Analysis and Recognition. Proceedings, Edinburgh, pp 958\u2013963"},{"key":"1895_CR28","unstructured":"Krizhevsky A,Sutskever I, Hinton GE ImageNet Classification with Deep Convolutional Neural Networks. NIPS. (2012)"},{"key":"1895_CR29","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. ICLR"},{"key":"1895_CR30","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep Residual Learning for Image Recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1895_CR31","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Maaten L VD (2017) Densely Connected Convolutional Networks.CVPR. IEEE Computer Society","DOI":"10.1109\/CVPR.2017.243"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01895-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10489-020-01895-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01895-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T03:43:43Z","timestamp":1617939823000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10489-020-01895-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,2]]},"references-count":31,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["1895"],"URL":"https:\/\/doi.org\/10.1007\/s10489-020-01895-x","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,2]]},"assertion":[{"value":"2 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}