{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T13:33:48Z","timestamp":1743082428257,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031220609"},{"type":"electronic","value":"9783031220616"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-22061-6_9","type":"book-chapter","created":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T20:02:58Z","timestamp":1670961778000},"page":"116-127","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Metric Learning on\u00a0Complex Projective Spaces"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9932-9409","authenticated-orcid":false,"given":"Yujin","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4219-7860","authenticated-orcid":false,"given":"Mohamed","family":"Daoudi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,14]]},"reference":[{"doi-asserted-by":"publisher","unstructured":"Daoudi, M., Hammal, Z., Kacem, A., Cohn, J.F.: Gram matrices formulation of body shape motion: an application for depression severity assessment. In: 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), pp. 258\u2013263 (2019). https:\/\/doi.org\/10.1109\/ACIIW.2019.8925009","key":"9_CR1","DOI":"10.1109\/ACIIW.2019.8925009"},{"doi-asserted-by":"publisher","unstructured":"Daoudi, M., Otberdout, N., Paiva, J.-C.\u00c1.: Metric learning on the manifold of oriented ellipses: application to facial expression recognition. In: Del Bimbo, A., (eds.) ICPR 2021. LNCS, vol. 12666, pp. 196\u2013206. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-68780-9_18","key":"9_CR2","DOI":"10.1007\/978-3-030-68780-9_18"},{"unstructured":"Graepel, T., Herbrich, R., Bollmann-Sdorra, P., Obermayer, K.: Classification on pairwise proximity data. In: Kearns, M., Solla, S., Cohn, D. (eds.) Advances in Neural Information Processing Systems, vol. 11. MIT Press (1999)","key":"9_CR3"},{"doi-asserted-by":"publisher","unstructured":"Gudmundsson, S., Runarsson, T.P., Sigurdsson, S.: Support vector machines and dynamic time warping for time series. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 2772\u20132776 (2008). https:\/\/doi.org\/10.1109\/IJCNN.2008.4634188","key":"9_CR4","DOI":"10.1109\/IJCNN.2008.4634188"},{"doi-asserted-by":"crossref","unstructured":"Hu, J., Lu, J., Tan, Y.P.: Discriminative deep metric learning for face verification in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1875\u20131882 (2014)","key":"9_CR5","DOI":"10.1109\/CVPR.2014.242"},{"doi-asserted-by":"crossref","unstructured":"Huang, Z., Wang, R., Shan, S., Chen, X.: Projection metric learning on grassmann manifold with application to video based face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 140\u2013149 (2015)","key":"9_CR6","DOI":"10.1109\/CVPR.2015.7298609"},{"doi-asserted-by":"publisher","unstructured":"Jain, S., Changbo Hu, Aggarwal, J.K.: Facial expression recognition with temporal modeling of shapes. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1642\u20131649. IEEE, Barcelona, Spain (2011). https:\/\/doi.org\/10.1109\/ICCVW.2011.6130446. https:\/\/ieeexplore.ieee.org\/document\/6130446\/","key":"9_CR7","DOI":"10.1109\/ICCVW.2011.6130446"},{"doi-asserted-by":"publisher","unstructured":"Jung, H., Lee, S., Yim, J., Park, S., Kim, J.: Joint fine-tuning in deep neural networks for facial expression recognition. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 2983\u20132991 (2015). https:\/\/doi.org\/10.1109\/ICCV.2015.341","key":"9_CR8","DOI":"10.1109\/ICCV.2015.341"},{"doi-asserted-by":"publisher","unstructured":"Kacem, A., Daoudi, M., Alvarez-Paiva, J.C.: Barycentric representation and metric learning for facial expression recognition. In: 2018 13th IEEE International Conference on Automatic Face Gesture Recognition (FG 2018), pp. 443\u2013447 (2018). https:\/\/doi.org\/10.1109\/FG.2018.00071","key":"9_CR9","DOI":"10.1109\/FG.2018.00071"},{"unstructured":"Kacem, A., Daoudi, M., Amor, B.B., Berretti, S., Alvarez-Paiva, J.C.: A novel geometric framework on gram matrix trajectories for human behavior understanding. arXiv:1807.00676 (2018)","key":"9_CR10"},{"issue":"2","key":"9_CR11","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1112\/blms\/16.2.81","volume":"16","author":"DG Kendall","year":"1984","unstructured":"Kendall, D.G.: Shape manifolds, procrustean metrics, and complex projective spaces. Bull. Lond. Math. Soc. 16(2), 81\u2013121 (1984). https:\/\/doi.org\/10.1112\/blms\/16.2.81","journal-title":"Bull. Lond. Math. Soc."},{"doi-asserted-by":"crossref","unstructured":"Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2197\u20132206 (2015)","key":"9_CR12","DOI":"10.1109\/CVPR.2015.7298832"},{"doi-asserted-by":"publisher","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. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, pp. 94\u2013101 (2010). https:\/\/doi.org\/10.1109\/CVPRW.2010.5543262","key":"9_CR13","DOI":"10.1109\/CVPRW.2010.5543262"},{"issue":"86","key":"9_CR14","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(86), 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"9_CR15","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s11263-015-0861-6","volume":"118","author":"L Shao","year":"2016","unstructured":"Shao, L., Liu, L., Yu, M.: Kernelized multiview projection for robust action recognition. Int. J. Comput. Vision 118(2), 115\u2013129 (2016). https:\/\/doi.org\/10.1007\/s11263-015-0861-6","journal-title":"Int. J. Comput. Vision"},{"doi-asserted-by":"publisher","unstructured":"Szczapa, B., Daoudi, M., Berretti, S., Pala, P., Bimbo, A.D., Hammal, Z.: Automatic estimation of self-reported pain by interpretable representations of motion dynamics. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 2544\u20132550 (2021). https:\/\/doi.org\/10.1109\/ICPR48806.2021.9412292","key":"9_CR16","DOI":"10.1109\/ICPR48806.2021.9412292"},{"doi-asserted-by":"publisher","unstructured":"Taheri, S., Turaga, P., Chellappa, R.: Towards view-invariant expression analysis using analytic shape manifolds. In: Face and Gesture 2011, pp. 306\u2013313. IEEE, Santa Barbara, CA, USA (Mar 2011). https:\/\/doi.org\/10.1109\/FG.2011.5771415","key":"9_CR17","DOI":"10.1109\/FG.2011.5771415"},{"doi-asserted-by":"crossref","unstructured":"Tanfous, A.B., Drira, H., Amor, B.B.: Sparse coding of shape trajectories for facial expression and action recognition. arXiv:1908.03231 (2019)","key":"9_CR18","DOI":"10.1109\/TPAMI.2019.2932979"},{"issue":"137","key":"9_CR19","first-page":"1","volume":"17","author":"J Townsend","year":"2016","unstructured":"Townsend, J., Koep, N., Weichwald, S.: Pymanopt: a python toolbox for optimization on manifolds using automatic differentiation. J. Mach. Learn. Res. 17(137), 1\u20135 (2016)","journal-title":"J. Mach. Learn. Res."},{"issue":"5","key":"9_CR20","doi-asserted-by":"publisher","first-page":"1859","DOI":"10.1016\/j.patcog.2013.11.025","volume":"47","author":"S Wan","year":"2014","unstructured":"Wan, S., Aggarwal, J.: Spontaneous facial expression recognition: a robust metric learning approach. Pattern Recogn. 47(5), 1859\u20131868 (2014). https:\/\/doi.org\/10.1016\/j.patcog.2013.11.025","journal-title":"Pattern Recogn."},{"doi-asserted-by":"publisher","unstructured":"Wang, Z., Wang, S., Ji, Q.: Capturing complex spatio-temporal relations among facial muscles for facial expression recognition. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3422\u20133429. IEEE, Portland, OR, USA (2013). https:\/\/doi.org\/10.1109\/CVPR.2013.439","key":"9_CR21","DOI":"10.1109\/CVPR.2013.439"},{"key":"9_CR22","first-page":"207","volume":"10","author":"KQ Weinberger","year":"2009","unstructured":"Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10, 207\u2013244 (2009)","journal-title":"J. Mach. Learn. Res."},{"issue":"9","key":"9_CR23","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1016\/j.imavis.2011.07.002","volume":"29","author":"G Zhao","year":"2011","unstructured":"Zhao, G., Huang, X., Taini, M., Li, S.Z., Pietik\u00e4inen, M.: Facial expression recognition from near-infrared videos. Image Vis. Comput. 29(9), 607\u2013619 (2011). https:\/\/doi.org\/10.1016\/j.imavis.2011.07.002","journal-title":"Image Vis. Comput."}],"container-title":["Lecture Notes in Computer Science","Smart Multimedia"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-22061-6_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T07:05:49Z","timestamp":1675321549000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-22061-6_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031220609","9783031220616"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-22061-6_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"14 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Smart Multimedia","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marseille","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icsm2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/smartmultimedia.org\/2021\/node\/3","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":"Papercept","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"68","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":"30","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":"4","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":"44% - 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":"8","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)"}}]}}