{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T23:16:28Z","timestamp":1779146188860,"version":"3.51.4"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031042447","type":"print"},{"value":"9783031042454","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-04245-4_33","type":"book-chapter","created":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T23:28:38Z","timestamp":1651706918000},"page":"370-382","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-feature Fusion Network Acts on Facial Expression Recognition"],"prefix":"10.1007","author":[{"given":"Jingyu","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiyue","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahao","family":"Geng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kezheng","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,5]]},"reference":[{"issue":"2","key":"33_CR1","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":"33_CR2","unstructured":"Lutao, G.: Research on Driver State Analysis Method Based on Facial Expression. University of Electronic Science and Technology of China (2019)"},{"issue":"1\u20133","key":"33_CR3","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","volume":"2","author":"S Wold","year":"1987","unstructured":"Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemom. Intell. Lab. Syst. 2(1\u20133), 37\u201352 (1987)","journal-title":"Chemom. Intell. Lab. Syst."},{"issue":"3","key":"33_CR4","doi-asserted-by":"publisher","first-page":"6719","DOI":"10.3390\/s150306719","volume":"15","author":"Q Jia","year":"2015","unstructured":"Jia, Q., Gao, X., Guo, H., et al.: Multi-layer sparse representation for weighted LBP-patches based facial expression recognition. Sensors 15(3), 6719\u20136739 (2015)","journal-title":"Sensors"},{"issue":"6","key":"33_CR5","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1016\/j.imavis.2008.08.005","volume":"27","author":"C Shan","year":"2009","unstructured":"Shan, C., Gong, S., Mcowan, P.W.: Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis. Comput. 27(6), 803\u2013816 (2009)","journal-title":"Image Vis. Comput."},{"issue":"1","key":"33_CR6","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1134\/S1054661815040070","volume":"26","author":"J Zhou","year":"2016","unstructured":"Zhou, J., Zhang, S., Mei, H., et al.: A method of facial expression recognition based on Gabor and NMF. Patt. Recognit. Image Anal. 26(1), 119\u2013124 (2016)","journal-title":"Patt. Recognit. Image Anal."},{"issue":"1","key":"33_CR7","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.patcog.2011.05.006","volume":"45","author":"W Gu","year":"2012","unstructured":"Gu, W., Xiang, C., Venkatesh, Y.V., et al.: Facial expression recognition using radial encoding of local Gabor features and classifier synthesis. Patt. Recogn. 45(1), 80\u201391 (2012)","journal-title":"Patt. Recogn."},{"key":"33_CR8","doi-asserted-by":"crossref","unstructured":"Wang, X., Jin, C., Liu, W., et al.: Feature fusion of HOG and WLD for facial expression recognition. In: IEEE\/SICE International Symposium on System Integration. IEEE (2014)","DOI":"10.1109\/SII.2013.6776664"},{"key":"33_CR9","doi-asserted-by":"publisher","unstructured":"Liu, P., Zhou, J.T., Tsang, I.W.H., et al.: Feature disentangling machine-a novel approach of feature selection and disentangling in facial expression analysis. In: European Conference on Computer Vision, pp. 151\u2013166. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10593-2_11","DOI":"10.1007\/978-3-319-10593-2_11"},{"issue":"5","key":"33_CR10","first-page":"592","volume":"51","author":"Q Fulan","year":"2019","unstructured":"Fulan, Q., Jianhong, L., Shu, Z., et al.: Rating recommendation based on deep hybrid model. J. Nanjing Univ. Aeronaut. Astronaut. 51(5), 592\u2013598 (2019)","journal-title":"J. Nanjing Univ. Aeronaut. Astronaut."},{"key":"33_CR11","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1097\u20131105 (2012)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"12","key":"33_CR12","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1016\/j.patcog.2016.07.026","volume":"61","author":"AT Lopes","year":"2017","unstructured":"Lopes, A.T., de Aguiar, E., De Souza, A.F., et al.: Facial expression recognition with convolutional neural networks: coping with few data and the training sample order. Pattern Recogn. 61(12), 610\u2013628 (2017)","journal-title":"Pattern Recogn."},{"issue":"3","key":"33_CR13","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1109\/TAFFC.2017.2753235","volume":"9","author":"G Pons","year":"2017","unstructured":"Pons, G., Masip, D.: Supervised committee of convolutional neural networks in automated facial expression analysis. IEEE Trans. Affect. Comput. 9(3), 343\u2013350 (2017)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"33_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: Convolutional Block Attention Module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1"},{"key":"33_CR15","doi-asserted-by":"crossref","unstructured":"Zeng, G., Zhou, J., Jia, X., et al.: Hand-Crafted feature guided deep learning for facial expression recognition. In: Proceedings of 2018 13th IEEE International Conference on Automatic Face&Gesture Recognition (FG 2018), Xi\u2019an, pp. 423\u2013430. IEEE (2018)","DOI":"10.1109\/FG.2018.00068"},{"issue":"4","key":"33_CR16","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1007\/s12559-019-09654-y","volume":"11","author":"X Sun","year":"2019","unstructured":"Sun, X., Lv, M.: Facial expression recognition based on a hybrid model combining deep and shallow features. Cogn. Comput. 11(4), 587\u2013597 (2019)","journal-title":"Cogn. Comput."},{"issue":"1","key":"33_CR17","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.jvcir.2018.11.010","volume":"59","author":"F Wang","year":"2019","unstructured":"Wang, F., Lv, J., Ying, G., et al.: Facial expression recognition from image based on hybrid features understanding. J. Vis. Commun. Image Represent. 59(1), 84\u201388 (2019)","journal-title":"J. Vis. Commun. Image Represent."},{"issue":"6","key":"33_CR18","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1049\/iet-cvi.2015.0037","volume":"10","author":"A Rikhtegar","year":"2016","unstructured":"Rikhtegar, A., Pooyan, M., Manzuri-Shalmani, M.T.: Genetic algorithm-optimised structure of convolutional neural network for face recognition applications. IET Comput. Vis. 10(6), 559\u2013566 (2016)","journal-title":"IET Comput. Vis."},{"issue":"07","key":"33_CR19","first-page":"2213","volume":"38","author":"GAO Jingwen","year":"2021","unstructured":"Jingwen, G.A.O., Yongxiang, C.A.I.: TP-FER: Three-channel facial expression recognition method based on optimized convolutional neural network. Appl. Res. Comput. 38(07), 2213\u20132219 (2021)","journal-title":"Appl. Res. Comput."},{"key":"33_CR20","doi-asserted-by":"publisher","unstructured":"Goodfellow, I.J., Erhan, D., Carrier, P., et al.: Challenges in representation learning: a report on three machine learning contests. In: Proceedings of International Conference on Neural Information Processing, pp. 117\u2013124. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-42051-1_16","DOI":"10.1007\/978-3-642-42051-1_16"},{"key":"33_CR21","doi-asserted-by":"crossref","unstructured":"Lucey, P., Cohn, J.F., Kanade, T.J., et al.: The extended Cohn-Kanade dataset (CK+) a complete dataset for action unit and emotion-specified expression. In: Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, pp. 94\u2013101. IEEE (2010)","DOI":"10.1109\/CVPRW.2010.5543262"},{"issue":"4","key":"33_CR22","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1049\/el.2018.7871","volume":"55","author":"H Ma","year":"2019","unstructured":"Ma, H., Celik, T.: FER-Net: facial expression recognition using densely connected convolutional network. Electron. Lett. 55(4), 184\u2013186 (2019)","journal-title":"Electron. Lett."},{"key":"33_CR23","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1016\/j.neucom.2017.08.043","volume":"273","author":"N Zeng","year":"2018","unstructured":"Zeng, N., Zhang, H., Song, B., et al.: Facial expression recognition via learning deep sparse autoencoders. Neurocomputing 273, 643\u2013649 (2018)","journal-title":"Neurocomputing"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","6GN for Future Wireless Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-04245-4_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T23:35:21Z","timestamp":1651707321000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-04245-4_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031042447","9783031042454"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-04245-4_33","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"value":"1867-8211","type":"print"},{"value":"1867-822X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"5 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"6GN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on 5G for Future Wireless Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Huizhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"gwn2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/5gwn.eai-conferences.org\/2021\/","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":"EAI Confy +","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"136","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":"63","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":"46% - 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)"}}]}}