{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T16:28:57Z","timestamp":1747153737107,"version":"3.40.5"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,10,24]],"date-time":"2021-10-24T00:00:00Z","timestamp":1635033600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,10,24]],"date-time":"2021-10-24T00:00:00Z","timestamp":1635033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s00521-021-06632-0","type":"journal-article","created":{"date-parts":[[2021,10,24]],"date-time":"2021-10-24T08:02:37Z","timestamp":1635062557000},"page":"3633-3645","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Robust affect analysis using committee of deep convolutional neural networks"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0835-5848","authenticated-orcid":false,"given":"Newlin Shebiah","family":"Russel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arivazhagan","family":"Selvaraj","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,24]]},"reference":[{"key":"6632_CR1","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1111\/j.1467-9280.1992.tb00253.x","volume":"3","author":"P Ekman","year":"1992","unstructured":"Ekman P (1992) Facial expressions of emotion: New findings, new questions. Psychol Sci 3:34\u201338","journal-title":"Psychol Sci"},{"key":"6632_CR2","doi-asserted-by":"crossref","unstructured":"Mattela G, Gupta SK (2018) Facial expression recognition using gabor-mean-DWT feature extraction technique. In: Proceedings of the 5th international conference on signal processing and integrated networks (SPIN), Noida, India, 22\u201323 February 2018 pp 575\u2013580","DOI":"10.1109\/SPIN.2018.8474206"},{"key":"6632_CR3","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1134\/S1054661807040190","volume":"17","author":"X Feng","year":"2007","unstructured":"Feng X, Pietik\u00e4inen M, Hadid A (2007) Facial expression recognition based on local binary patterns. Pattern Recognit Image Anal 17:592\u2013598","journal-title":"Pattern Recognit Image Anal"},{"key":"6632_CR4","doi-asserted-by":"publisher","first-page":"1657","DOI":"10.1109\/TIP.2010.2044957","volume":"19","author":"Z Guo","year":"2010","unstructured":"Guo Z, Zhang L, Zhang D (2010) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process 19:1657\u20131663","journal-title":"IEEE Trans Image Process"},{"key":"6632_CR5","doi-asserted-by":"publisher","first-page":"784","DOI":"10.4218\/etrij.10.1510.0132","volume":"32","author":"T Jabid","year":"2010","unstructured":"Jabid T, Kabir MH, Chae O (2010) Robust facial expression recognition based on local directional pattern. ETRI J 32:784\u2013794","journal-title":"ETRI J"},{"key":"6632_CR6","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.patrec.2008.03.014","volume":"30","author":"P Yang","year":"2009","unstructured":"Yang P, Liu Q, Metaxas DN (2009) Boosting encoded dynamic features for facial expression recognition. Pattern Recognit Lett 30:132\u2013139","journal-title":"Pattern Recognit Lett"},{"key":"6632_CR7","doi-asserted-by":"crossref","unstructured":"Cohn JF, Zlochower AJ,Lien JJ, Kanade T (1998) Feature-point tracking by optical flow discriminates subtle differences in facial expression. In: Proceedings of the third IEEE international conference on automatic face and gesture recognition, Nara, Japan, 14\u201316 April 1998 p 396","DOI":"10.1109\/AFGR.1998.670981"},{"key":"6632_CR8","first-page":"028","volume":"7","author":"Y Liu","year":"2011","unstructured":"Liu Y, Wang JD, Li P (2011) A feature point tracking method based on the combination of SIFT algorithm and KLT matching algorithm. J Astronaut 7:028","journal-title":"J Astronaut"},{"key":"6632_CR9","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097\u20131105","journal-title":"Adv Neural Inf Process Syst"},{"key":"6632_CR10","unstructured":"Simonyan K, Zisserman, A (2014) Very deep convolutional networks for large-scale image recognition,\u201d arXiv preprint arXiv:1409.1556"},{"key":"6632_CR11","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov V, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"6632_CR12","doi-asserted-by":"crossref","unstructured":"Parkhi OM, Vedaldi A, Zisserman et al A (2015) Deep face recognition. In BMVC vol 1 no 3: p. 6","DOI":"10.5244\/C.29.41"},{"key":"6632_CR13","doi-asserted-by":"crossref","unstructured":"Levi G, Hassner T (2015) Emotion recognition in the wild via convolutional neural networks and mapped binary patterns. In: Proceedings of the 2015 ACM on international conference on multimodal interaction. ACM, pp 503\u2013510","DOI":"10.1145\/2818346.2830587"},{"key":"6632_CR14","doi-asserted-by":"crossref","unstructured":"Lowe DG (1999) Object recognition from local scale-invariant features. In: Computer vision, 1999. The proceedings of the seventh IEEE international conference on, IEEE, 1999, vol 2: pp 1150\u20131157.","DOI":"10.1109\/ICCV.1999.790410"},{"issue":"12","key":"6632_CR15","doi-asserted-by":"publisher","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, Yan K (2016) A deep neural network-driven feature learning method for multi-view facial expression recognition. IEEE Trans Multimed 18(12):2528\u20132536","journal-title":"IEEE Trans Multimed"},{"key":"6632_CR16","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.ins.2017.10.044","volume":"428","author":"L Chen","year":"2018","unstructured":"Chen L, Zhou M, Su W, Wu M, She J, Hirota K (2018) Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction. Inf Sci 428:49\u201361","journal-title":"Inf Sci"},{"key":"6632_CR17","doi-asserted-by":"crossref","unstructured":"Ciregan D, Meier U, Schmidhuber J (2012) Multi-column deep neural networks for image classification. In: Computer vision and pattern recognition (CVPR), 2012 IEEE conference on. IEEE, 2012, pp 3642\u2013 3649","DOI":"10.1109\/CVPR.2012.6248110"},{"key":"6632_CR18","doi-asserted-by":"crossref","unstructured":"Zhao X, Liang X, Liu L, Li T, Han Y, Vasconcelos N, Yan S (2016) Peak-piloted deep network for facial expression recognition. In: European conference on computer vision, Springer pp 425\u2013 442","DOI":"10.1007\/978-3-319-46475-6_27"},{"key":"6632_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00371-017-1458-6","volume":"34","author":"J Zhao","year":"2018","unstructured":"Zhao J, Mao X, Zhang J (2018) Learning deep facial expression features from image and optical flow sequences using 3d cnn. Visual Comput 34:1\u201315","journal-title":"Visual Comput"},{"key":"6632_CR20","doi-asserted-by":"crossref","unstructured":"Jung H, Lee S, Yim J, Park S, Kim J (2015) Joint fine-tuning in deep neural networks for facial expression recognition. In: Computer vision (ICCV), 2015 IEEE international conference on. IEEE pp 2983\u20132991","DOI":"10.1109\/ICCV.2015.341"},{"key":"6632_CR21","doi-asserted-by":"crossref","unstructured":"Yan J, Zheng W, Cui Z, Tang C, Zhang T, Zong Y, Sun N (2016) Multi-clue fusion for emotion recognition in the wild. In: Proceedings of the 18th ACM International conference on multimodal interaction. ACM, pp 458\u2013463","DOI":"10.1145\/2993148.2997630"},{"key":"6632_CR22","doi-asserted-by":"crossref","unstructured":"Donahue J, Anne Hendricks L, Guadarrama S, Rohrbach M, Venugopalan S, Saenko K, Darrell (2015) Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE conference on computer vision and pattern recognition pp 2625\u20132634.","DOI":"10.1109\/CVPR.2015.7298878"},{"key":"6632_CR23","doi-asserted-by":"crossref","unstructured":"Martin O, Kotsia I, Macq B, Pitas I (2006) The eNTERFACE\u201905 AudioVisual Emotion Database. In Presented at the 22nd international conference on data engineering workshops (ICDEW\u201906); IEEE","DOI":"10.1109\/ICDEW.2006.145"},{"key":"6632_CR24","unstructured":"Haq S, Jackson PJ (2009) Speaker-dependent audio-visual emotion recognition. In: Proceedings of the international conference on auditory-visual speech processing"},{"key":"6632_CR25","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/MMUL.2012.26","volume":"19","author":"A Dhall","year":"2012","unstructured":"Dhall A, Goecke R, Lucey S, Gedeon T (2012) Collecting large, richly annotated facial-expression databases from movies. IEEE Multimed 19:34\u201341","journal-title":"IEEE Multimed"},{"issue":"2","key":"6632_CR26","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s11042-009-0344-2","volume":"49","author":"M Mansoorizadeh","year":"2010","unstructured":"Mansoorizadeh M, Charkari NM (2010) Multimodal information fusion application to human emotion recognition from face and speech. Multimed Tools Appl 49(2):277\u2013297","journal-title":"Multimed Tools Appl"},{"key":"6632_CR27","unstructured":"Datcu D, Rothkrantz L (2009) Multimodal recognition of emotions in car environments. In: Proceedings of the conference on driver-car interaction & interface (DCI&I), Prague, pp 1\u20139"},{"key":"6632_CR28","doi-asserted-by":"crossref","unstructured":"Mansoorizadeh\u00a0M, Charkari NM (2008) Bimodal person-dependent emotion recognition comparison of feature level and decision level information fusion. In: Proceedings of the 1st ACM international conference on PErvasive technologies related to assistive environments, Athens, Greece, article no. 90, pp 1\u20134","DOI":"10.1145\/1389586.1389688"},{"issue":"2","key":"6632_CR29","first-page":"14","volume":"6","author":"M Bejani","year":"2012","unstructured":"Bejani M, Gharavian D, Charkari NM (2012) Facial expression recognition using temporal templates. Majlesi J Electr Eng 6(2):14\u201320","journal-title":"Majlesi J Electr Eng"},{"key":"6632_CR30","doi-asserted-by":"publisher","first-page":"1269","DOI":"10.1007\/s00371-012-0768-y","volume":"29","author":"M Rashid","year":"2013","unstructured":"Rashid M, Abu-Bakar SAR, Mokji M (2013) Human emotion recognition from videos using spatio-temporal and audio features. Vis Comput 29:1269\u20131275","journal-title":"Vis Comput"},{"issue":"1","key":"6632_CR31","doi-asserted-by":"publisher","first-page":"53","DOI":"10.5772\/54002","volume":"10","author":"S Dobri\u0161ek","year":"2013","unstructured":"Dobri\u0161ek S, Gaj\u0161ek R, Miheli\u010d F, Pave\u0161i\u0107 N, \u0160truc V (2013) Towards efficient multi-modal emotion recognition. Int J Adv Robot Syst 10(1):53","journal-title":"Int J Adv Robot Syst"},{"key":"6632_CR32","doi-asserted-by":"crossref","unstructured":"Poria S, Cambria\u00a0E, Gelbukh\u00a0A (2015) Deep convolutional neural network textual features and multiple kernel learning for utterance-level multimodal sentiment analysis. In: Proceedings of the international conference on empirical methods in natural language processing, Lisbon, Portugal, pp 2539\u20132544.","DOI":"10.18653\/v1\/D15-1303"},{"key":"6632_CR33","doi-asserted-by":"publisher","first-page":"48807","DOI":"10.1109\/ACCESS.2019.2907271","volume":"7","author":"X Pan","year":"2019","unstructured":"Pan X, Ying G, Chen G, Li H, Li W (2019) A deep spatial and temporal aggregation framework for video-based facial expression recognition. IEEE Access 7:48807\u201348815","journal-title":"IEEE Access"},{"issue":"4","key":"6632_CR34","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1002\/ima.22338","volume":"29","author":"A Selvaraj","year":"2019","unstructured":"Selvaraj A, Russel NS (2019) Bimodal recognition of affective states with the features inspired from human visual and auditory perception system. Int J Imaging Syst Technol 29(4):584\u2013598","journal-title":"Int J Imaging Syst Technol"},{"key":"6632_CR35","unstructured":"Banda N, Robinson P (2011) Noise analysis in audio-visual emotion recognition. In: Proceedings of the 11th international conference on multimodal interaction, ACM Press, New York pp 1\u20134"},{"issue":"5","key":"6632_CR36","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1177\/1059712316664017","volume":"24","author":"P Barros","year":"2016","unstructured":"Barros P, Wermter S (2016) Developing crossmodal expression recognition based on a deep neural model. Adapt Behav 24(5):373\u2013396","journal-title":"Adapt Behav"},{"key":"6632_CR37","doi-asserted-by":"crossref","unstructured":"Almaev TR, Y\u00fcce\u00a0A, Ghitulescu A, Valstar MF (2013) Distribution-based iterative pairwise classification of emotions in the wild using LGBP-TOP. In: Proceedings of the 15th ACM International conference on multimodal interaction, ACM Press, Sydney, Australia pp 535\u2013542","DOI":"10.1145\/2522848.2531742"},{"key":"6632_CR38","doi-asserted-by":"crossref","unstructured":"Gehrig T, Ekenel\u00a0HK (2013) Why is facial expression analysis in the wild challenging?In: Proceedings of the 2013 on emotion recognition in the wild challenge and workshop, ACM Press, Sydney, Australia, pp 9\u201316","DOI":"10.1145\/2531923.2531924"},{"key":"6632_CR39","doi-asserted-by":"crossref","unstructured":"Kaya H, G\u00fcrpinar F, Afshar S, Salah AA (2015) Contrasting and combining least squares based learners for emotion recognition in the wild. In: Proceedings of the 2015 ACM on international conference on multimodal interaction, ACM Press, Seattle, Washington, USA pp 459\u2013466","DOI":"10.1145\/2818346.2830588"},{"key":"6632_CR40","doi-asserted-by":"crossref","unstructured":"Ding W, Xu M, Huang D, Lin W, Dong M, Yu X, Li H (2016) Audio and face video emotion recognition in the wild using deep neural networks and small datasets. In: Proceedings of the 18th ACM international conference on multimodal interaction, ACM Press, Tokyo, Japan pp 506\u2013513","DOI":"10.1145\/2993148.2997637"},{"key":"6632_CR41","doi-asserted-by":"crossref","unstructured":"Ghazi MM, Ekenel HK (2016) Automatic emotion recognition in the wild using an ensemble of static and dynamic representations. In: Proceedings of the 18th ACM international conference on multimodal interaction, ACM Press, Tokyo, Japan pp 514\u2013521","DOI":"10.1145\/2993148.2997634"},{"key":"6632_CR42","doi-asserted-by":"crossref","unstructured":"Pini S, Ahmed OB, Cornia\u00a0M, Baraldi\u00a0L, Cucchiara R, Huet B (2017) Modeling multimodal cues in a deep learning-based framework for emotion recognition in the wild. In: Proceedings of the 19th ACM international conference on multimodal interaction, ACM Press, Glasgow, UK, pp 536\u2013543","DOI":"10.1145\/3136755.3143006"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06632-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06632-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06632-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T06:35:17Z","timestamp":1645684517000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06632-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,24]]},"references-count":42,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["6632"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06632-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2021,10,24]]},"assertion":[{"value":"14 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 October 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 October 2021","order":3,"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"}}]}}