{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T06:04:37Z","timestamp":1743055477193,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030645588"},{"type":"electronic","value":"9783030645595"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-64559-5_49","type":"book-chapter","created":{"date-parts":[[2020,12,11]],"date-time":"2020-12-11T18:04:01Z","timestamp":1607709841000},"page":"618-632","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Emotion Categorization from Video-Frame Images Using a Novel Sequential Voting Technique"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9689-3290","authenticated-orcid":false,"given":"Harisu Abdullahi","family":"Shehu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8979-2224","authenticated-orcid":false,"given":"Will","family":"Browne","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0521-2630","authenticated-orcid":false,"given":"Hedwig","family":"Eisenbarth","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,12,7]]},"reference":[{"key":"49_CR1","doi-asserted-by":"publisher","unstructured":"Tian, Y.-L., Kanade, T., Cohn, J.F.: Facial expression analysis. In: Handbook of Face Recognition, pp. 247\u2013275. Springer, New York (2005). https:\/\/doi.org\/10.1007\/0-387-27257-7_12","DOI":"10.1007\/0-387-27257-7_12"},{"key":"49_CR2","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-319-25958-1_4","volume-title":"Advances in Face Detection and Facial Image Analysis","author":"B Martinez","year":"2016","unstructured":"Martinez, B., Valstar, M.F.: Advances, challenges, and opportunities in automatic facial expression recognition. In: Kawulok, M., Celebi, M.E., Smolka, B. (eds.) Advances in Face Detection and Facial Image Analysis, pp. 63\u2013100. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-25958-1_4"},{"issue":"2","key":"49_CR3","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1007\/s11031-011-9212-2","volume":"35","author":"D Matsumoto","year":"2011","unstructured":"Matsumoto, D., Hwang, H.S.: Evidence for training the ability to read microexpressions of emotion. Motiv. Emot. 35(2), 181\u2013191 (2011)","journal-title":"Motiv. Emot."},{"key":"49_CR4","doi-asserted-by":"crossref","unstructured":"Krumhuber, E.G., K\u00fcster, D., Namba, S., Shah, D., Calvo, M.G.: Emotion recognition from posed and spontaneous dynamic expressions: human observers versus machine analysis. Emotion (2019)","DOI":"10.31234\/osf.io\/x5uht"},{"issue":"1","key":"49_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1177\/1529100619832930","volume":"20","author":"LF Barrett","year":"2019","unstructured":"Barrett, L.F., Adolphs, R., Marsella, S., Martinez, A.M., Pollak, S.D.: Emotional expressions reconsidered: challenges to inferring emotion from human facial movements. Psychol. Sci. Public Interest 20(1), 1\u201368 (2019)","journal-title":"Psychol. Sci. Public Interest"},{"issue":"9","key":"49_CR6","doi-asserted-by":"publisher","first-page":"a023358","DOI":"10.1101\/cshperspect.a023358","volume":"7","author":"A Chakravarti","year":"2015","unstructured":"Chakravarti, A.: Perspectives on human variation through the lens of diversity and race. Cold Spring Harb. Perspect. Biol. 7(9), a023358 (2015)","journal-title":"Cold Spring Harb. Perspect. Biol."},{"key":"49_CR7","doi-asserted-by":"crossref","unstructured":"Islam, B., Mahmud, F., Hossain, A.: Facial region segmentation based emotion recognition using extreme learning machine. In: 2018 International Conference on Advancement in Electrical and Electronic Engineering, ICAEEE, pp. 1\u20134 (2019)","DOI":"10.1109\/ICAEEE.2018.8642990"},{"key":"49_CR8","doi-asserted-by":"crossref","unstructured":"Mahmud, F., Islam, B., Hossain, A., Goala, P.B.: Facial region segmentation based emotion recognition using K-nearest neighbors. In: 2018 International Conference on Innovation in Engineering and Technology, ICIET 2018, pp. 1\u20135 (2019)","DOI":"10.1109\/CIET.2018.8660900"},{"key":"49_CR9","doi-asserted-by":"crossref","unstructured":"Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I.: The extended Cohn-Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010, pp. 94\u2013101 (2010)","DOI":"10.1109\/CVPRW.2010.5543262"},{"issue":"2","key":"49_CR10","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1109\/T-AFFC.2013.4","volume":"4","author":"SM Mavadati","year":"2013","unstructured":"Mavadati, S.M., Mahoor, M.H., Bartlett, K., Trinh, P., Cohn, J.F.: DISFA: a spontaneous facial action intensity database. IEEE Trans. Affect. Comput. 4(2), 151\u2013160 (2013)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"49_CR11","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1016\/j.neuropsychologia.2013.01.022","volume":"51","author":"CA Longmore","year":"2013","unstructured":"Longmore, C.A., Tree, J.J.: Motion as a cue to face recognition: evidence from congenital prosopagnosia. Neuropsychologia 51, 864\u2013875 (2013)","journal-title":"Neuropsychologia"},{"issue":"2","key":"49_CR12","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1037\/h0030377","volume":"17","author":"P Ekman","year":"1971","unstructured":"Ekman, P., Friesen, W.V.: Constants across cultures in the face and emotion. J. Pers. Soc. Psychol. 17(2), 124 (1971)","journal-title":"J. Pers. Soc. Psychol."},{"key":"49_CR13","doi-asserted-by":"crossref","unstructured":"Mollahosseini, A., Chan, D., Mahoor, M.H.: Going deeper in facial expression recognition using deep neural networks. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, pp. 1\u201310 (2016)","DOI":"10.1109\/WACV.2016.7477450"},{"key":"49_CR14","unstructured":"Minaee, S., Abdolrashidi, A.: Deep-emotion: facial expression recognition using attentional convolutional network. arXiv preprint arXiv:1902.01019 (2019)"},{"key":"49_CR15","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.patcog.2015.07.006","volume":"49","author":"S Elaiwat","year":"2016","unstructured":"Elaiwat, S., Bennamoun, M., Boussaid, F.: A spatio-temporal RBM-based model for facial expression recognition. Pattern Recognit. 49, 152\u2013161 (2016)","journal-title":"Pattern Recognit."},{"key":"49_CR16","doi-asserted-by":"publisher","first-page":"41273","DOI":"10.1109\/ACCESS.2019.2907327","volume":"7","author":"JH Kim","year":"2019","unstructured":"Kim, J.H., Kim, B.G., Roy, P.P., Jeong, D.M.: Efficient facial expression recognition algorithm based on hierarchical deep neural network structure. IEEE Access 7, 41273\u201341285 (2019)","journal-title":"IEEE Access"},{"issue":"1","key":"49_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TAFFC.2014.2386334","volume":"6","author":"SL Happy","year":"2015","unstructured":"Happy, S.L., Routray, A.: Automatic facial expression recognition using features of salient facial patches. IEEE Trans. Affect. Comput. 6(1), 1\u201312 (2015)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"49_CR18","doi-asserted-by":"publisher","first-page":"1330","DOI":"10.3389\/fpsyg.2016.01330","volume":"7","author":"R Xiao","year":"2016","unstructured":"Xiao, R., Li, X., Li, L., Wang, Y.: Can we distinguish emotions from faces? Investigation of implicit and explicit processes of peak facial expressions. Front. Psychol. 7, 1330 (2016). (1664\u20131078)","journal-title":"Front. Psychol."},{"key":"49_CR19","unstructured":"Kanade, T., Cohn, J.F., Tian, Y.: Comprehensive database for facial expression analysis. In: Proceedings - 4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000, March, pp. 46\u201353 (2000)"},{"key":"49_CR20","doi-asserted-by":"crossref","unstructured":"Ting, G., Moydin, K., Hamdulla, A.: An overview of feature extraction methods for handwritten image retrieval. In: Proceedings - 2018 3rd International Conference on Smart City and Systems Engineering, ICSCSE 2018, pp. 840\u2013843 (2018)","DOI":"10.1109\/ICSCSE.2018.00181"},{"key":"49_CR21","doi-asserted-by":"crossref","unstructured":"Pisal, A., Sor, R., Kinage, K.S.: Facial feature extraction using hierarchical max(HMAX) method. In: 2017 International Conference on Computing, Communication, Control and Automation, ICCUBEA 2017, (figure 2), pp. 1\u20135 (2018)","DOI":"10.1109\/ICCUBEA.2017.8463755"},{"key":"49_CR22","doi-asserted-by":"crossref","unstructured":"Loussaief, S., Abdelkrim, A.: Machine learning framework for image classification. In: 2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications, SETIT 2016, pp. 58\u201361 (2017)","DOI":"10.1109\/SETIT.2016.7939841"},{"issue":"7","key":"49_CR23","doi-asserted-by":"publisher","first-page":"2559","DOI":"10.1109\/TIP.2013.2253483","volume":"22","author":"Y Li","year":"2013","unstructured":"Li, Y., Wang, S., Zhao, Y., Ji, Q.: Simultaneous facial feature tracking and facial expression recognition. IEEE Trans. Image Process. 22(7), 2559\u20132573 (2013)","journal-title":"IEEE Trans. Image Process."},{"key":"49_CR24","doi-asserted-by":"crossref","unstructured":"Cruz, A.C., Bhanu, B., Thakoor, N.S.: One shot emotion scores for facial emotion recognition. In: 2014 IEEE International Conference on Image Processing, ICIP 2014, (C), pp. 1376\u20131380 (2014)","DOI":"10.1109\/ICIP.2014.7025275"},{"issue":"3","key":"49_CR25","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1109\/21.97458","volume":"21","author":"SR Safavian","year":"1991","unstructured":"Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybernet. 21(3), 660\u2013674 (1991)","journal-title":"IEEE Trans. Syst. Man Cybernet."},{"key":"49_CR26","doi-asserted-by":"crossref","unstructured":"Shehu, H.A., Browne, W., Eisenbarth, H.: An adversarial attacks resistance-based approach to emotion recognition from images using facial landmarks. In: 2020 IEEE International Conference on Robot and Human Interactive Communication (2020)","DOI":"10.1109\/RO-MAN47096.2020.9223510"},{"key":"49_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/978-3-540-71457-6_51","volume-title":"Computer Vision\/Computer Graphics Collaboration Techniques","author":"ASM Sohail","year":"2007","unstructured":"Sohail, A.S.M., Bhattacharya, P.: Classification of facial expressions using K-nearest neighbor classifier. In: Gagalowicz, A., Philips, W. (eds.) MIRAGE 2007. LNCS, vol. 4418, pp. 555\u2013566. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-71457-6_51"},{"issue":"1","key":"49_CR28","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s10100-017-0479-6","volume":"26","author":"B Kami\u0144ski","year":"2017","unstructured":"Kami\u0144ski, B., Jakubczyk, M., Szufel, P.: A framework for sensitivity analysis of decision trees. Central Eur. J. Oper. Res. 26(1), 135\u2013159 (2017). https:\/\/doi.org\/10.1007\/s10100-017-0479-6","journal-title":"Central Eur. J. Oper. Res."},{"key":"49_CR29","doi-asserted-by":"crossref","unstructured":"Shehu, H.A., Tokat, S., Sharif, M.H., Uyaver, S.: Sentiment analysis of Turkish Twitter data. In: AIP Conference Proceedings, vol. 2183, no. 1, p. 080004. AIP Publishing LLC, December 2019","DOI":"10.1063\/1.5136197"},{"key":"49_CR30","series-title":"Lecture Notes on Data Engineering and Communications Technologies","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1007\/978-3-030-36178-5_15","volume-title":"Artificial Intelligence and Applied Mathematics in Engineering Problems","author":"HA Shehu","year":"2020","unstructured":"Shehu, H.A., Tokat, S.: A hybrid approach for the sentiment analysis of Turkish Twitter data. In: Hemanth, D.J., Kose, U. (eds.) ICAIAME 2019. LNDECT, vol. 43, pp. 182\u2013190. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-36178-5_15"},{"issue":"1","key":"49_CR31","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"issue":"3","key":"49_CR32","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","volume":"46","author":"NS Altman","year":"1992","unstructured":"Altman, N.S.: An introduction to kernel and nearest-neighbor non-parametric regression. Am. Stat. 46(3), 175\u2013185 (1992)","journal-title":"Am. Stat."},{"key":"49_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/978-3-030-01418-6_9","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2018","author":"Y Fan","year":"2018","unstructured":"Fan, Y., Lam, J.C.K., Li, V.O.K.: Multi-region Ensemble Convolutional Neural Network for Facial Expression Recognition. In: K\u016frkov\u00e1, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11139, pp. 84\u201394. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01418-6_9"},{"key":"49_CR34","doi-asserted-by":"crossref","unstructured":"Chengeta, K., Viriri, S.: A review of local, holistic and deep learning approaches in facial expressions Recognition. In 2019 Conference on Information Communications Technology and Society (ICTAS), pp. 1\u20137. IEEE, March 2019","DOI":"10.1109\/ICTAS.2019.8703521"}],"container-title":["Lecture Notes in Computer Science","Advances in Visual Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-64559-5_49","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,18]],"date-time":"2024-08-18T22:58:35Z","timestamp":1724021915000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-64559-5_49"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030645588","9783030645595"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-64559-5_49","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"7 December 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISVC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Visual Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"San Diego, CA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isvc2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.isvc.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"175","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"114","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"65% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The symposium was held virtually due to the COVID-19 pandemic. 65 papers were accepted as oral presentations and 41 as posters. 12 special tracks papers are also included.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}