{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T22:07:59Z","timestamp":1743113279886,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031581809"},{"type":"electronic","value":"9783031581816"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-58181-6_44","type":"book-chapter","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T10:03:42Z","timestamp":1719914622000},"page":"523-531","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Face Emotion Recognition with\u00a0FACS-Based Synthetic Dataset Using Deep Learning Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7945-0992","authenticated-orcid":false,"given":"Shiwangi","family":"Mishra","sequence":"first","affiliation":[]},{"given":"P.","family":"Shalu","sequence":"additional","affiliation":[]},{"given":"Rohan","family":"Singh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,3]]},"reference":[{"key":"44_CR1","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1007\/s10586-017-0832-5","volume":"21","author":"S Arshid","year":"2018","unstructured":"Arshid, S., Hussain, A., Munir, A., Nawaz, A., Aziz, S.: Multi-stage binary patterns for facial expression recognition in real world. Clust. Comput. 21, 323\u2013331 (2018)","journal-title":"Clust. Comput."},{"key":"44_CR2","doi-asserted-by":"crossref","unstructured":"Ekman, P., Friesen, W.V.: Facial action coding system. Environ. Psychol. Nonverbal Behav. (1978)","DOI":"10.1037\/t27734-000"},{"issue":"45\u201360","key":"44_CR3","first-page":"16","volume":"98","author":"P Ekman","year":"1999","unstructured":"Ekman, P., et al.: Basic emotions. Handb. Cogn. Emot. 98(45\u201360), 16 (1999)","journal-title":"Handb. Cogn. Emot."},{"key":"44_CR4","series-title":"Lecture Notes in Electrical Engineering","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.1007\/978-981-16-3690-5_136","volume-title":"ICDSMLA 2020","author":"DKR Gaddam","year":"2022","unstructured":"Gaddam, D.K.R., Ansari, M.D., Vuppala, S., Gunjan, V.K., Sati, M.M.: Human facial emotion detection using deep learning. In: Kumar, A., Senatore, S., Gunjan, V.K. (eds.) ICDSMLA 2020. LNEE, vol. 783, pp. 1417\u20131427. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-16-3690-5_136"},{"key":"44_CR5","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.1016\/j.procs.2023.01.108","volume":"218","author":"C Gautam","year":"2023","unstructured":"Gautam, C., Seeja, K.: Facial emotion recognition using handcrafted features and CNN. Procedia Comput. Sci. 218, 1295\u20131303 (2023)","journal-title":"Procedia Comput. Sci."},{"key":"44_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/978-3-642-42051-1_16","volume-title":"Neural Information Processing","author":"IJ Goodfellow","year":"2013","unstructured":"Goodfellow, I.J., et al.: Challenges in representation learning: a report on three machine learning contests. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013. LNCS, vol. 8228, pp. 117\u2013124. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-42051-1_16"},{"key":"44_CR7","unstructured":"Khaireddin, Y., Chen, Z.: Facial emotion recognition: state of the art performance on FER2013. arXiv preprint arXiv:2105.03588 (2021)"},{"issue":"15","key":"44_CR8","doi-asserted-by":"publisher","first-page":"6195","DOI":"10.1016\/j.ijleo.2016.04.015","volume":"127","author":"SA Khan","year":"2016","unstructured":"Khan, S.A., Hussain, A., Usman, M.: Facial expression recognition on real world face images using intelligent techniques: a survey. Optik 127(15), 6195\u20136203 (2016)","journal-title":"Optik"},{"issue":"2","key":"44_CR9","doi-asserted-by":"publisher","first-page":"401","DOI":"10.3390\/s18020401","volume":"18","author":"BC Ko","year":"2018","unstructured":"Ko, B.C.: A brief review of facial emotion recognition based on visual information. Sensors 18(2), 401 (2018)","journal-title":"Sensors"},{"key":"44_CR10","unstructured":"Komala, K., Jayadevappa, D., Shivaprakash, G.: Human emotion detection and classification using convolution neural network. Eur. J. Mol. Clin. Med. 7(06) (2020)"},{"key":"44_CR11","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, pp. 94\u2013101 (2010)","DOI":"10.1109\/CVPRW.2010.5543262"},{"key":"44_CR12","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1016\/j.procs.2020.07.101","volume":"175","author":"W Mellouk","year":"2020","unstructured":"Mellouk, W., Handouzi, W.: Facial emotion recognition using deep learning: review and insights. Procedia Comput. Sci. 175, 689\u2013694 (2020)","journal-title":"Procedia Comput. Sci."},{"issue":"9","key":"44_CR13","doi-asserted-by":"publisher","first-page":"3046","DOI":"10.3390\/s21093046","volume":"21","author":"S Minaee","year":"2021","unstructured":"Minaee, S., Minaei, M., Abdolrashidi, A.: Deep-emotion: facial expression recognition using attentional convolutional network. Sensors 21(9), 3046 (2021)","journal-title":"Sensors"},{"key":"44_CR14","doi-asserted-by":"crossref","unstructured":"Ng, H.W., Nguyen, V.D., Vonikakis, V., Winkler, S.: Deep learning for emotion recognition on small datasets using transfer learning, pp. 443\u2013449 (2015)","DOI":"10.1145\/2818346.2830593"},{"key":"44_CR15","volume-title":"What the Face Reveals: Basic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS)","author":"EL Rosenberg","year":"2020","unstructured":"Rosenberg, E.L., Ekman, P.: What the Face Reveals: Basic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS). Oxford University Press, Oxford (2020)"},{"key":"44_CR16","doi-asserted-by":"publisher","first-page":"95","DOI":"10.3389\/fnins.2019.00095","volume":"13","author":"A Sengupta","year":"2019","unstructured":"Sengupta, A., Ye, Y., Wang, R., Liu, C., Roy, K.: Going deeper in spiking neural networks: VGG and residual architectures. Front. Neurosci. 13, 95 (2019)","journal-title":"Front. Neurosci."},{"key":"44_CR17","doi-asserted-by":"crossref","unstructured":"Sinha, D., El-Sharkawy, M.: Thin MobileNet: an enhanced MobileNet architecture, pp. 0280\u20130285 (2019)","DOI":"10.1109\/UEMCON47517.2019.8993089"},{"issue":"5","key":"44_CR18","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1007\/s11760-021-02079-x","volume":"16","author":"P Sreevidya","year":"2022","unstructured":"Sreevidya, P., Veni, S., Ramana Murthy, O.: Elder emotion classification through multimodal fusion of intermediate layers and cross-modal transfer learning. Signal Image Video Process. 16(5), 1281\u20131288 (2022)","journal-title":"Signal Image Video Process."},{"key":"44_CR19","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1016\/j.neucom.2016.12.043","volume":"230","author":"Y Sun","year":"2017","unstructured":"Sun, Y., Wen, G.: Cognitive facial expression recognition with constrained dimensionality reduction. Neurocomputing 230, 397\u2013408 (2017)","journal-title":"Neurocomputing"},{"key":"44_CR20","series-title":"Lecture Notes in Networks and Systems","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1007\/978-3-030-84760-9_19","volume-title":"Second International Conference on Image Processing and Capsule Networks","author":"S Sureddy","year":"2022","unstructured":"Sureddy, S., Jacob, J.: Multi-features based multi-layer perceptron for facial expression recognition system. In: Chen, J.I.-Z., Tavares, J.M.R.S., Iliyasu, A.M., Du, K.-L. (eds.) ICIPCN 2021. LNNS, vol. 300, pp. 206\u2013217. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-84760-9_19"},{"key":"44_CR21","unstructured":"Targ, S., Almeida, D., Lyman, K.: Resnet in resnet: generalizing residual architectures. arXiv preprint arXiv:1603.08029 (2016)"},{"key":"44_CR22","unstructured":"Wasi, A.T., \u0160erbetar, K., Islam, R., Rafi, T.H., Chae, D.K.: ARBEx: attentive feature extraction with reliability balancing for robust facial expression learning. arXiv preprint arXiv:2305.01486 (2023)"}],"container-title":["Communications in Computer and Information Science","Computer Vision and Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-58181-6_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T10:13:38Z","timestamp":1719915218000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-58181-6_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031581809","9783031581816"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-58181-6_44","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Vision and Image Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jammu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"3 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cvip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iitjammu.ac.in\/cvip2023\/","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":"Online CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"461","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":"140","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":"30% - 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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}