{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T11:48:37Z","timestamp":1769341717955,"version":"3.49.0"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031781247","type":"print"},{"value":"9783031781254","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T00:00:00Z","timestamp":1733356800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T00:00:00Z","timestamp":1733356800000},"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\/978-3-031-78125-4_28","type":"book-chapter","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T06:07:47Z","timestamp":1733292467000},"page":"412-426","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Dynamic Resolution Guidance for\u00a0Facial Expression Recognition"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1159-2043","authenticated-orcid":false,"given":"Songpan","family":"Wang","sequence":"first","affiliation":[]},{"given":"Xu","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,5]]},"reference":[{"key":"28_CR1","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 18th ACM International Conference on Multimodal Interaction, pp. 279\u2013283 (2016)","DOI":"10.1145\/2993148.2993165"},{"key":"28_CR2","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1016\/j.ins.2021.10.005","volume":"582","author":"FZ Canal","year":"2022","unstructured":"Canal, F.Z., et al.: A survey on facial emotion recognition techniques: a state-of-the-art literature review. Inf. Sci. 582, 593\u2013617 (2022)","journal-title":"Inf. Sci."},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Cheng, B., et al.: Robust emotion recognition from low quality and low bit rate video: a deep learning approach. In: 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 65\u201370. IEEE (2017)","DOI":"10.1109\/ACII.2017.8273580"},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Dai, T., Cai, J., Zhang, Y., Xia, S.T., Zhang, L.: Second-order attention network for single image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11065\u201311074 (2019)","DOI":"10.1109\/CVPR.2019.01132"},{"key":"28_CR5","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"},{"key":"28_CR6","unstructured":"Hilles, M.M., Naser, S.S.A.: Knowledge-based intelligent tutoring system for teaching mongo database.(2017) (2017)"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Hu, X., Mu, H., Zhang, X., Wang, Z., Tan, T., Sun, J.: Meta-SR: a magnification-arbitrary network for super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1575\u20131584 (2019)","DOI":"10.1109\/CVPR.2019.00167"},{"key":"28_CR8","unstructured":"Jing, W., Tian, F., Zhang, J., Chao, K.M., Hong, Z., Liu, X.: Feature super-resolution based facial expression recognition for multi-scale low-resolution faces. arXiv preprint arXiv:2004.02234 (2020)"},{"key":"28_CR9","doi-asserted-by":"crossref","unstructured":"Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: Deep Laplacian pyramid networks for fast and accurate super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 624\u2013632 (2017)","DOI":"10.1109\/CVPR.2017.618"},{"key":"28_CR10","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"},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Mu\u00a0Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136\u2013144 (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"28_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1007\/978-3-030-37734-2_43","volume-title":"MultiMedia Modeling","author":"Z Liu","year":"2020","unstructured":"Liu, Z., Li, L., Wu, Y., Zhang, C.: Facial expression restoration based on improved graph convolutional networks. In: Ro, Y.M., et al. (eds.) MMM 2020. LNCS, vol. 11962, pp. 527\u2013539. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-37734-2_43"},{"key":"28_CR13","doi-asserted-by":"crossref","unstructured":"Lukas, S., Mitra, A.R., Desanti, R.I., Krisnadi, D.: Student attendance system in classroom using face recognition technique. In: 2016 International Conference on Information and Communication Technology Convergence (ICTC), pp. 1032\u20131035. IEEE (2016)","DOI":"10.1109\/ICTC.2016.7763360"},{"key":"28_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107678","volume":"236","author":"F Nan","year":"2022","unstructured":"Nan, F., et al.: Feature super-resolution based facial expression recognition for multi-scale low-resolution images. Knowl.-Based Syst. 236, 107678 (2022)","journal-title":"Knowl.-Based Syst."},{"key":"28_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1007\/978-3-030-60639-8_52","volume-title":"Pattern Recognition and Computer Vision","author":"J Ou","year":"2020","unstructured":"Ou, J., Wu, H.: Efficient human pose estimation with Depthwise separable convolution and person centroid guided joint grouping. In: Peng, Y., et al. (eds.) PRCV 2020. LNCS, vol. 12306, pp. 626\u2013638. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-60639-8_52"},{"key":"28_CR16","unstructured":"Paszke, A., et\u00a0al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"28_CR17","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1007\/s10489-020-01855-5","volume":"51","author":"J Shao","year":"2021","unstructured":"Shao, J., Cheng, Q.: E-FCNN for tiny facial expression recognition. Appl. Intell. 51, 549\u2013559 (2021)","journal-title":"Appl. Intell."},{"key":"28_CR18","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.\u00a01\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"28_CR19","doi-asserted-by":"crossref","unstructured":"Tang, J., Zhou, X., Zheng, J.: Design of intelligent classroom facial recognition based on deep learning. In: Journal of Physics: Conference Series. vol.\u00a01168, p. 022043. IOP Publishing (2019)","DOI":"10.1088\/1742-6596\/1168\/2\/022043"},{"key":"28_CR20","unstructured":"Wu, G., Jiang, J., Liu, X., Ma, J.: A practical contrastive learning framework for single image super-resolution. arXiv preprint arXiv:2111.13924 (2021)"},{"key":"28_CR21","unstructured":"Zhang, Y., Liu, T., Long, M., Jordan, M.: Bridging theory and algorithm for domain adaptation. In: International Conference on Machine Learning, pp. 7404\u20137413. PMLR (2019)"},{"key":"28_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 286\u2013301 (2018)","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"28_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2472\u20132481 (2018)","DOI":"10.1109\/CVPR.2018.00262"},{"key":"28_CR24","first-page":"27319","volume":"34","author":"M Zhu","year":"2021","unstructured":"Zhu, M., et al.: Dynamic resolution network. Adv. Neural. Inf. Process. Syst. 34, 27319\u201327330 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78125-4_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T07:08:58Z","timestamp":1733296138000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78125-4_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,5]]},"ISBN":["9783031781247","9783031781254"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78125-4_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,5]]},"assertion":[{"value":"5 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}