{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T22:40:11Z","timestamp":1744152011405,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030739720"},{"type":"electronic","value":"9783030739737"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-73973-7_27","type":"book-chapter","created":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T14:03:24Z","timestamp":1617977004000},"page":"282-291","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep Residual Neural Network for Child\u2019s Spontaneous Facial Expressions Recognition"],"prefix":"10.1007","author":[{"given":"Abdul","family":"Qayyum","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Imran","family":"Razzak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,4,10]]},"reference":[{"key":"27_CR1","doi-asserted-by":"crossref","unstructured":"Abbasnejad, I., Sridharan, S., Nguyen, D., Denman, S., Fookes, C., Lucey, S.: Using synthetic data to improve facial expression analysis with 3D convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 1609\u20131618 (2017)","DOI":"10.1109\/ICCVW.2017.189"},{"key":"27_CR2","doi-asserted-by":"publisher","unstructured":"Al Chanti, D.A., Caplier, A.: Deep learning for spatio-temporal modeling of dynamic spontaneous emotions. IEEE Trans. Affect. Comput. (2018). https:\/\/doi.org\/10.1109\/TAFFC.2018.2873600","DOI":"10.1109\/TAFFC.2018.2873600"},{"key":"27_CR3","doi-asserted-by":"crossref","unstructured":"Chen, J., Konrad, J., Ishwar, P.: VGAN-based image representation learning for privacy-preserving facial expression recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1570\u20131579 (2018)","DOI":"10.1109\/CVPRW.2018.00207"},{"key":"27_CR4","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1007\/978-3-030-53337-3_32","volume-title":"Business Information Systems","author":"A Cano Montes","year":"2020","unstructured":"Cano Montes, A., Hern\u00e1ndez G\u00f3mez, L.A.: Audio-visual emotion recognition system for variable length spatio-temporal samples using deep transfer-learning. In: Abramowicz, W., Klein, G. (eds.) BIS 2020. LNBIP, vol. 389, pp. 434\u2013446. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-53337-3_32"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Fan, Y., Lu, X., Li, D., Liu, Y.: Video-based emotion recognition using CNN-RNN and C3D hybrid networks. In: Proceedings of the 18th ACM International Conference on Multimodal Interaction, pp. 445\u2013450 (2016)","DOI":"10.1145\/2993148.2997632"},{"key":"27_CR6","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026\u20131034 (2015)","DOI":"10.1109\/ICCV.2015.123"},{"issue":"6","key":"27_CR7","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1037\/a0021383","volume":"78","author":"SM Jones","year":"2010","unstructured":"Jones, S.M., Brown, J.L., Hoglund, W.L.G., Aber, J.L.: A school-randomized clinical trial of an integrated social-emotional learning and literacy intervention: impacts after 1 school year. J. Consult. Clin. Psychol. 78(6), 829 (2010)","journal-title":"J. Consult. Clin. Psychol."},{"issue":"2","key":"27_CR8","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1111\/j.1467-8624.2010.01560.x","volume":"82","author":"SM Jones","year":"2011","unstructured":"Jones, S.M., Brown, J.L., Aber, J.L.: Two-year impacts of a universal school-based social-emotional and literacy intervention: an experiment in translational developmental research. Child Dev. 82(2), 533\u2013554 (2011)","journal-title":"Child Dev."},{"key":"27_CR9","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.imavis.2019.02.004","volume":"83","author":"RA Khan","year":"2019","unstructured":"Khan, R.A., Crenn, A., Meyer, A., Bouakaz, S.: A novel database of children\u2019s spontaneous facial expressions (LIRIS-CSE). Image Vis. Comput. 83, 61\u201369 (2019)","journal-title":"Image Vis. Comput."},{"issue":"2","key":"27_CR10","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1109\/TAFFC.2017.2695999","volume":"10","author":"DH Kim","year":"2017","unstructured":"Kim, D.H., Baddar, W.J., Jang, J., Ro, Y.M.: Multi-objective based spatio-temporal feature representation learning robust to expression intensity variations for facial expression recognition. IEEE Trans. Affect. Comput. 10(2), 223\u2013236 (2017)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Lai, Y.-H., Lai, S.-H.: Emotion-preserving representation learning via generative adversarial network for multi-view facial expression recognition. In: 2018 13th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2018), pp. 263\u2013270. IEEE (2018)","DOI":"10.1109\/FG.2018.00046"},{"key":"27_CR12","first-page":"306","volume":"46","author":"DJ McDowell","year":"2000","unstructured":"McDowell, D.J., O\u2019Neil, R., Parke, R.D.: Display rule application in a disappointing situation and children\u2019s emotional reactivity: relations with social competence. Merrill-Palmer Q. (1982-) 46, 306\u2013324 (2000)","journal-title":"Merrill-Palmer Q. (1982-)"},{"key":"27_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/978-3-030-63836-8_21","volume-title":"Neural Information Processing","author":"A Qayyum","year":"2020","unstructured":"Qayyum, A., Razzak, I., Mumtaz, W.: Hybrid deep shallow network for assessment of depression using electroencephalogram signals. In: Yang, H., Pasupa, K., Leung, A.C.-S., Kwok, J.T., Chan, J.H., King, I. (eds.) ICONIP 2020. LNCS, vol. 12534, pp. 245\u2013257. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-63836-8_21"},{"issue":"6","key":"27_CR14","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.1109\/TNSRE.2019.2913142","volume":"27","author":"I Razzak","year":"2019","unstructured":"Razzak, I., Blumenstein, M., Guandong, X.: Multiclass support matrix machines by maximizing the inter-class margin for single trial EEG classification. IEEE Trans. Neural Syst. Rehabil. Eng. 27(6), 1117\u20131127 (2019)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"27_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JTEHM.2019.2942017","volume":"7","author":"I Razzak","year":"2019","unstructured":"Razzak, I., Hameed, I.A., Xu, G.: Robust sparse representation and multiclass support matrix machines for the classification of motor imagery EEG signals. IEEE J. Trans. Eng. Health Med. 7, 1\u20138 (2019)","journal-title":"IEEE J. Trans. Eng. Health Med."},{"key":"27_CR16","doi-asserted-by":"publisher","unstructured":"Razzak, I., Naz, S.: Unit-vise: deep shallow unit-vise residual neural networks with transition layer for expert level skin cancer classification. IEEE\/ACM Trans. Comput. Biol. Bioinform. (2020). https:\/\/doi.org\/10.1109\/TCBB.2020.3039358","DOI":"10.1109\/TCBB.2020.3039358"},{"key":"27_CR17","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.dr.2015.05.001","volume":"37","author":"M Sprung","year":"2015","unstructured":"Sprung, M., M\u00fcnch, H.M., Harris, P.L., Ebesutani, C., Hofmann, S.G.: Children\u2019s emotion understanding: a meta-analysis of training studies. Dev. Rev. 37, 41\u201365 (2015)","journal-title":"Dev. Rev."},{"key":"27_CR18","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4489\u20134497 (2015)","DOI":"10.1109\/ICCV.2015.510"},{"key":"27_CR19","doi-asserted-by":"crossref","unstructured":"Vielzeuf, V., Pateux, S., Jurie, F.: Temporal multimodal fusion for video emotion classification in the wild. In: Proceedings of the 19th ACM International Conference on Multimodal Interaction, pp. 569\u2013576 (2017)","DOI":"10.1145\/3136755.3143011"},{"key":"27_CR20","doi-asserted-by":"crossref","unstructured":"Yang, H., Zhang, Z., Yin, L.: Identity-adaptive facial expression recognition through expression regeneration using conditional generative adversarial networks. In: 2018 13th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2018), pp. 294\u2013301. IEEE (2018)","DOI":"10.1109\/FG.2018.00050"},{"key":"27_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, F., Zhang, T., Mao, Q., Xu, C.: Joint pose and expression modeling for facial expression recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3359\u20133368 (2018)","DOI":"10.1109\/CVPR.2018.00354"},{"issue":"10","key":"27_CR22","doi-asserted-by":"publisher","first-page":"1461","DOI":"10.1007\/s00371-018-1477-y","volume":"34","author":"J Zhao","year":"2018","unstructured":"Zhao, J., Mao, X., Zhang, J.: Learning deep facial expression features from image and optical flow sequences using 3D CNN. Vis. Comput. 34(10), 1461\u20131475 (2018)","journal-title":"Vis. Comput."}],"container-title":["Lecture Notes in Computer Science","Structural, Syntactic, and Statistical Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-73973-7_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T22:02:20Z","timestamp":1744149740000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-73973-7_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030739720","9783030739737"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-73973-7_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"10 April 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"S+SSPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 January 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 January 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sspr2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dais.unive.it\/sspr2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"81","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":"35","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":"0","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":"43% - 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":"4","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)"}}]}}