{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T18:12:26Z","timestamp":1764785546179,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819620609"},{"type":"electronic","value":"9789819620616"}],"license":[{"start":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T00:00:00Z","timestamp":1735603200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T00:00:00Z","timestamp":1735603200000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-2061-6_13","type":"book-chapter","created":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T05:46:03Z","timestamp":1735537563000},"page":"169-182","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Intra-class Compact Facial Expression Recognition Based on\u00a0Amplitude Phase Separation"],"prefix":"10.1007","author":[{"given":"Xiang","family":"Tian","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang","family":"Mu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,31]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1109\/LSP.2024.3364055","volume":"31","author":"R Dong","year":"2024","unstructured":"Dong, R., Lam, K.M.: Bi-center loss for compound facial expression recognition. IEEE Signal Process. Lett. 31, 641\u2013645 (2024)","journal-title":"IEEE Signal Process. Lett."},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Le, N., Nguyen, K., Tran, Q., Tjiputra, E., Le, B., Nguyen, A.: Uncertainty-aware label distribution learning for facial expression recognition. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision, pp. 6088\u20136097 (2023)","DOI":"10.1109\/WACV56688.2023.00603"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Liu, S., Xu, Y., Wan, T., Kui, X.: A dual-branch adaptive distribution fusion framework for real-world facial expression recognition. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp.\u00a01\u20135 (2023)","DOI":"10.1109\/ICASSP49357.2023.10097033"},{"issue":"2","key":"13_CR4","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/s11263-021-01556-7","volume":"130","author":"D Ruan","year":"2022","unstructured":"Ruan, D., Mo, R., Yan, Y., Chen, S., Xue, J.H., Wang, H.: Adaptive deep disturbance-disentangled learning for facial expression recognition. Int. J. Comput. Vision 130(2), 455\u2013477 (2022)","journal-title":"Int. J. Comput. Vision"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Xing, Z., Tan, W., He, R., Lin, Y., Yan, B.: Co-completion for occluded facial expression recognition. In: Proceedings of the ACM International Conference on Multimedia, pp. 130\u2013140 (2022)","DOI":"10.1145\/3503161.3548183"},{"key":"13_CR6","doi-asserted-by":"crossref","unstructured":"Mo, R., Yan, Y., Xue, J.H., Chen, S., Wang, H.: D$$^3$$Net: dual-branch disturbance disentangling network for facial expression recognition. In: Proceedings of the ACM International Conference on Multimedia, pp. 779\u2013787 (2021)","DOI":"10.1145\/3474085.3475249"},{"issue":"4","key":"13_CR7","doi-asserted-by":"publisher","first-page":"1868","DOI":"10.1109\/TAFFC.2022.3197761","volume":"13","author":"J Jiang","year":"2022","unstructured":"Jiang, J., Deng, W.: Disentangling identity and pose for facial expression recognition. IEEE Trans. Affect. Comput. 13(4), 1868\u20131878 (2022)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Wang, K., Peng, X., Yang, J., Lu, S., Qiao, Y.: Suppressing uncertainties for large-scale facial expression recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6897\u20136906 (2020)","DOI":"10.1109\/CVPR42600.2020.00693"},{"key":"13_CR9","doi-asserted-by":"publisher","first-page":"4057","DOI":"10.1109\/TIP.2019.2956143","volume":"29","author":"K Wang","year":"2020","unstructured":"Wang, K., Peng, X., Yang, J., Meng, D., Qiao, Y.: Region attention networks for pose and occlusion robust facial expression recognition. IEEE Trans. Image Process. 29, 4057\u20134069 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Liang, G., Wang, S., Wang, C.: Pose-invariant facial expression recognition. In: Proceedings of the 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 01\u201308 (2021)","DOI":"10.1109\/FG52635.2021.9666974"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Wang, F., et al.: Dynamic-static graph convolutional network for video-based facial expression recognition. In: Proceedings of the International Conference on Multimedia Modeling, pp. 42\u201355 (2024)","DOI":"10.1007\/978-3-031-53308-2_4"},{"key":"13_CR12","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1016\/j.ins.2022.11.068","volume":"619","author":"C Liu","year":"2023","unstructured":"Liu, C., Hirota, K., Dai, Y.: Patch attention convolutional vision transformer for facial expression recognition with occlusion. Inf. Sci. 619, 781\u2013794 (2023)","journal-title":"Inf. Sci."},{"key":"13_CR13","doi-asserted-by":"publisher","first-page":"7216","DOI":"10.1109\/TII.2024.3353912","volume":"20","author":"Y Wang","year":"2024","unstructured":"Wang, Y., et al.: MGR$$^3$$Net: multigranularity region relation representation network for facial expression recognition in affective robots. IEEE Trans. Ind. Inform. 20, 7216\u20137226 (2024)","journal-title":"IEEE Trans. Ind. Inform."},{"key":"13_CR14","doi-asserted-by":"publisher","first-page":"1800","DOI":"10.1109\/TMM.2021.3072786","volume":"24","author":"F Zhang","year":"2021","unstructured":"Zhang, F., Xu, M., Xu, C.: Weakly-supervised facial expression recognition in the wild with noisy data. IEEE Trans. Multimedia 24, 1800\u20131814 (2021)","journal-title":"IEEE Trans. Multimedia"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, X., Zhang, Z., Xu, X.: Lightweight multi-level information fusion network for facial expression recognition. In: Proceedings of the International Conference on Multimedia Modeling, pp. 151\u2013163 (2023)","DOI":"10.1007\/978-3-031-27818-1_13"},{"key":"13_CR16","doi-asserted-by":"publisher","first-page":"6544","DOI":"10.1109\/TIP.2021.3093397","volume":"30","author":"Z Zhao","year":"2021","unstructured":"Zhao, Z., Liu, Q., Wang, S.: Learning deep global multi-scale and local attention features for facial expression recognition in the wild. IEEE Trans. Image Process. 30, 6544\u20136556 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Wu, Y., Wang, S., Chang, Y.: Patch-aware representation learning for facial expression recognition. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 6143\u20136151 (2023)","DOI":"10.1145\/3581783.3612342"},{"issue":"3","key":"13_CR18","doi-asserted-by":"publisher","first-page":"2035","DOI":"10.1007\/s00371-023-02900-3","volume":"40","author":"H Xia","year":"2024","unstructured":"Xia, H., Lu, L., Song, S.: Feature fusion of multi-granularity and multi-scale for facial expression recognition. Vis. Comput. 40(3), 2035\u20132047 (2024)","journal-title":"Vis. Comput."},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"Huang, K., et al.: An efficient algorithm of facial expression recognition by TSG-RNN network. In: Proceedings of the International Conference on Multimedia Modeling, pp. 161\u2013174 (2020)","DOI":"10.1007\/978-3-030-37734-2_14"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Li, S., Deng, W., Du, J.: Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2852\u20132861 (2017)","DOI":"10.1109\/CVPR.2017.277"},{"issue":"03","key":"13_CR21","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/MMUL.2012.26","volume":"19","author":"A Dhall","year":"2012","unstructured":"Dhall, A., Goecke, R., Lucey, S., Gedeon, T.: Collecting large, richly annotated facial-expression databases from movies. IEEE Multimedia 19(03), 34\u201341 (2012)","journal-title":"IEEE Multimedia"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Barsoum, E., Zhang, C., Ferrer, C.C., Zhang, Z.: Training deep networks for facial expression recognition with crowd-sourced label distribution. In: Proceedings of the ACM International Conference on Multimodal Interaction, pp. 279\u2013283 (2016)","DOI":"10.1145\/2993148.2993165"},{"issue":"5","key":"13_CR23","doi-asserted-by":"publisher","first-page":"2439","DOI":"10.1109\/TIP.2018.2886767","volume":"28","author":"Y Li","year":"2018","unstructured":"Li, Y., Zeng, J., Shan, S., Chen, X.: Occlusion aware facial expression recognition using CNN with attention mechanism. IEEE Trans. Image Process. 28(5), 2439\u20132450 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4690\u20134699 (2019)","DOI":"10.1109\/CVPR.2019.00482"},{"key":"13_CR25","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"13_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/978-3-319-46487-9_6","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Y Guo","year":"2016","unstructured":"Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: MS-Celeb-1M: a dataset and benchmark for large-scale face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 87\u2013102. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_6"},{"key":"13_CR27","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"11","key":"13_CR29","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11), 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"13_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/03610927408827101","volume":"3","author":"T Cali\u0144ski","year":"1974","unstructured":"Cali\u0144ski, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. Theory Methods 3(1), 1\u201327 (1974)","journal-title":"Commun. Stat. Theory Methods"}],"container-title":["Lecture Notes in Computer Science","MultiMedia Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-2061-6_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T06:04:48Z","timestamp":1735538688000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-2061-6_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,31]]},"ISBN":["9789819620609","9789819620616"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-2061-6_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,31]]},"assertion":[{"value":"31 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MMM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Multimedia Modeling","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nara","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 January 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 January 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mmm2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mmm2025.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}