{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T11:28:22Z","timestamp":1750850902779,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031476365"},{"type":"electronic","value":"9783031476372"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-47637-2_19","type":"book-chapter","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T20:01:39Z","timestamp":1699128099000},"page":"243-257","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A New Lightweight Attention-Based Model for Emotion Recognition on Distorted Social Media Face Images"],"prefix":"10.1007","author":[{"given":"Ayush","family":"Roy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Palaiahnakote","family":"Shivakumara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Umapada","family":"Pal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shivanand S.","family":"Gornale","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng-Lin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"unstructured":"Goodfellow, I., Courville, A., Bengio, Y.: Large-scale feature learning with spike-and-slab sparse coding.\u00a0arXiv preprint arXiv:1206.6407 (2012)","key":"19_CR1"},{"issue":"4","key":"19_CR2","first-page":"1777","volume":"15","author":"S Vignesh","year":"2023","unstructured":"Vignesh, S., Savithadevi, M., Sridevi, M., Sridhar, R.: A novel facial emotion recognition model using segmentation VGG-19 architecture. Int. J. Inf. Technol. 15(4), 1777\u20131787 (2023)","journal-title":"Int. J. Inf. Technol."},{"key":"19_CR3","doi-asserted-by":"publisher","first-page":"26756","DOI":"10.1109\/ACCESS.2022.3156598","volume":"10","author":"AP Fard","year":"2022","unstructured":"Fard, A.P., Mahoor, M.H.: Ad-corre: adaptive correlation-based loss for facial expression recognition in the wild. IEEE Access 10, 26756\u201326768 (2022)","journal-title":"IEEE Access"},{"issue":"9","key":"19_CR4","doi-asserted-by":"publisher","first-page":"419","DOI":"10.3390\/info13090419","volume":"13","author":"R Pecoraro","year":"2022","unstructured":"Pecoraro, R., Basile, V., Bono, V.: Local multi-head channel self-attention for facial expression recognition. Information 13(9), 419 (2022)","journal-title":"Information"},{"unstructured":"Khaireddin, Y., Chen, Z.: Facial emotion recognition: state of the art performance on FER2013.\u00a0arXiv preprint arXiv:2105.03588 (2021)","key":"19_CR5"},{"unstructured":"Christopher, P., Martin, K.: Facial expression recognition using convolutional neural networks: state of the art. arXiv preprint arXiv:1612.02903 (2016)","key":"19_CR6"},{"doi-asserted-by":"crossref","unstructured":"Luan, P., The, H.V., Tuan, A.T.: Facial expression recognition using residual masking network. In 2020 25th International Conference on Pattern Recognition (ICPR), pp. 4513\u20134519. IEEE (2021)","key":"19_CR7","DOI":"10.1109\/ICPR48806.2021.9411919"},{"issue":"4","key":"19_CR8","doi-asserted-by":"publisher","first-page":"2132","DOI":"10.1109\/TAFFC.2022.3188390","volume":"13","author":"AV Savchenko","year":"2022","unstructured":"Savchenko, A.V., Savchenko, L.V., Makarov, I.: Classifying emotions and engagement in online learning based on a single facial expression recognition neural network. IEEE Trans. Affect. Comput. 13(4), 2132\u20132143 (2022)","journal-title":"IEEE Trans. Affect. Comput."},{"unstructured":"Kollias, D., Zafeiriou, S.: Expression, affect, action unit recognition: Aff-Wild2, multi-task learning and ArcFace. arXiv preprint arXiv:1910.04855 (2019)","key":"19_CR9"},{"unstructured":"Wen, Z., Lin, W., Wang, T., Xu, G.: Distract your attention: multi-head cross attention network for facial expression recognition.\u00a0arXiv preprint arXiv:2109.07270 (2021)","key":"19_CR10"},{"unstructured":"Pourmirzaei, M., Montazer, G. A., Esmaili, F.: Using self-supervised auxiliary tasks to improve fine-grained facial representation.\u00a0arXiv preprint arXiv:2105.06421 (2021)","key":"19_CR11"},{"key":"19_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patrec.2021.03.007","volume":"146","author":"L Schoneveld","year":"2021","unstructured":"Schoneveld, L., Othmani, A., Abdelkawy, H.: Leveraging recent advances in deep learning for audio-visual emotion recognition. Pattern Recogn. Lett. 146, 1\u20137 (2021)","journal-title":"Pattern Recogn. Lett."},{"doi-asserted-by":"publisher","unstructured":"Leong, S.C., Tang, Y.M., Lai, C.H., Lee, C.K.M.: Facial expression and body gesture emotions recognition: a systematic review on the use of visual data in affective computing. Comput. Sci. Rev. 48 (2023). https:\/\/doi.org\/10.1016\/j.cosrev.2023.100545","key":"19_CR13","DOI":"10.1016\/j.cosrev.2023.100545"},{"doi-asserted-by":"publisher","unstructured":"Liu, H., Cai, H., Lin, Q., Zhang, X., Li, X., Xiao, H.: FEDA: fine-grained emotion difference analysis for facial expression recognition. Biomed. Sig. Process. Control 79 (2023). https:\/\/doi.org\/10.1016\/j.bspc.2022.104209","key":"19_CR14","DOI":"10.1016\/j.bspc.2022.104209"},{"doi-asserted-by":"publisher","unstructured":"Verma, M., Mandal, M., Reddy, S.K., Meedimale, Y.R., Vipparthi, S.K.: Efficient neural architecture search for emotions recognition. Exp. Syst. Appl. 224 (2023). https:\/\/doi.org\/10.1016\/j.eswa.2023.119957","key":"19_CR15","DOI":"10.1016\/j.eswa.2023.119957"},{"unstructured":"Daquan, Z., Hou, Q., Chen, Y., Feng, J., Yan, S.: Rethinking bottleneck structure for efficient mobile network design. arXiv arXiv:2007.02269 (2020)","key":"19_CR16"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv arXiv:1512.03385 (2015)","key":"19_CR17","DOI":"10.1109\/CVPR.2016.90"},{"doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks. arXiv arXiv:1608.06993 (2016)","key":"19_CR18","DOI":"10.1109\/CVPR.2017.243"},{"doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J., Kweon, I.S.: CBAM: convolutional block attention module. arXiv arXiv:1807.06521 (2018)","key":"19_CR19","DOI":"10.1007\/978-3-030-01234-2_1"},{"doi-asserted-by":"crossref","unstructured":"Chen, T., et al.: ABD-Net: attentive but diverse person re-identification. In Proceedings of the ICCV, pp. 8350\u20138360 (2019)","key":"19_CR20","DOI":"10.1109\/ICCV.2019.00844"},{"unstructured":"Vaswani, A., et al.: Attention is all you need. arXiv arXiv:1706.03762 (2017)","key":"19_CR21"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-47637-2_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T11:29:48Z","timestamp":1730460588000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-47637-2_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031476365","9783031476372"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-47637-2_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kitakyushu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acpr2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ericlab.org\/acpr2023\/","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":"164","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":"93","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":"57% - 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":"2","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":"5","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)"}}]}}