{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T23:44:27Z","timestamp":1769730267575,"version":"3.49.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030377304","type":"print"},{"value":"9783030377311","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T00:00:00Z","timestamp":1577145600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-37731-1_27","type":"book-chapter","created":{"date-parts":[[2019,12,27]],"date-time":"2019-12-27T01:02:51Z","timestamp":1577408571000},"page":"329-340","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Classroom Attention Analysis Based on Multiple Euler Angles Constraint and Head Pose Estimation"],"prefix":"10.1007","author":[{"given":"Xin","family":"Xu","sequence":"first","affiliation":[]},{"given":"Xin","family":"Teng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,24]]},"reference":[{"issue":"1","key":"27_CR1","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/TSMCB.2008.927274","volume":"39","author":"SO Ba","year":"2008","unstructured":"Ba, S.O., Odobez, J.M.: Recognizing visual focus of attention from head pose in natural meetings. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 39(1), 16\u201333 (2008)","journal-title":"IEEE Trans. Syst. Man Cybern. Part B (Cybern.)"},{"key":"27_CR2","doi-asserted-by":"crossref","unstructured":"Bulat, A., Tzimiropoulos, G.: How far are we from solving the 2D & 3D face alignment problem? (And a dataset of 230,000 3D facial landmarks). In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1021\u20131030 (2017)","DOI":"10.1109\/ICCV.2017.116"},{"key":"27_CR3","doi-asserted-by":"crossref","unstructured":"Chang, F.J., Tuan Tran, A., Hassner, T., Masi, I., Nevatia, R., Medioni, G.: FacePoseNet: making a case for landmark-free face alignment. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1599\u20131608 (2017)","DOI":"10.1109\/ICCVW.2017.188"},{"key":"27_CR4","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Kazemi, V., Sullivan, J.: One millisecond face alignment with an ensemble of regression trees. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1867\u20131874 (2014)","DOI":"10.1109\/CVPR.2014.241"},{"key":"27_CR6","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"27_CR7","doi-asserted-by":"crossref","unstructured":"Kumar, A., Alavi, A., Chellappa, R.: KEPLER: keypoint and pose estimation of unconstrained faces by learning efficient H-CNN regressors. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pp. 258\u2013265. IEEE (2017)","DOI":"10.1109\/FG.2017.149"},{"key":"27_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21\u201337. Springer, Cham (2016). \nhttps:\/\/doi.org\/10.1007\/978-3-319-46448-0_2"},{"key":"27_CR9","unstructured":"Matsumoto, Y., Zelinsky, A.: An algorithm for real-time stereo vision implementation of head pose and gaze direction measurement. In: Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580), pp. 499\u2013504. IEEE (2000)"},{"key":"27_CR10","doi-asserted-by":"crossref","unstructured":"Najibi, M., Samangouei, P., Chellappa, R., Davis, L.S.: SSH: single stage headless face detector. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4875\u20134884 (2017)","DOI":"10.1109\/ICCV.2017.522"},{"key":"27_CR11","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.patcog.2017.06.009","volume":"71","author":"M Patacchiola","year":"2017","unstructured":"Patacchiola, M., Cangelosi, A.: Head pose estimation in the wild using convolutional neural networks and adaptive gradient methods. Pattern Recogn. 71, 132\u2013143 (2017)","journal-title":"Pattern Recogn."},{"key":"27_CR12","doi-asserted-by":"crossref","unstructured":"Qin, J., Zhou, Y., Lu, H., Ya, H.: Teaching video analytics based on student spatial and temporal behavior mining. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp. 635\u2013642. ACM (2015)","DOI":"10.1145\/2671188.2749357"},{"key":"27_CR13","doi-asserted-by":"crossref","unstructured":"Raca, M., Dillenbourg, P.: System for assessing classroom attention. In: Proceedings of 3rd International Learning Analytics & Knowledge Conference. No. CONF (2013)","DOI":"10.1145\/2460296.2460351"},{"key":"27_CR14","doi-asserted-by":"crossref","unstructured":"Raca, M., Dillenbourg, P.: Holistic analysis of the classroom. In: Proceedings of the 2014 ACM Workshop on Multimodal Learning Analytics Workshop and Grand Challenge, pp. 13\u201320. ACM (2014)","DOI":"10.1145\/2666633.2666636"},{"key":"27_CR15","doi-asserted-by":"crossref","unstructured":"Raca, M., Tormey, R., Dillenbourg, P.: Sleepers\u2019 lag-study on motion and attention. In: Proceedings of the Fourth International Conference on Learning Analytics and Knowledge, pp. 36\u201343. ACM (2014)","DOI":"10.1145\/2567574.2567581"},{"issue":"1","key":"27_CR16","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1109\/TPAMI.2017.2781233","volume":"41","author":"R Ranjan","year":"2017","unstructured":"Ranjan, R., Patel, V.M., Chellappa, R.: HyperFace: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Trans. Pattern Anal. Mach. Intell. 41(1), 121\u2013135 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"27_CR17","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91\u201399 (2015)"},{"key":"27_CR18","doi-asserted-by":"crossref","unstructured":"Ruiz, N., Chong, E., Rehg, J.M.: Fine-grained head pose estimation without keypoints. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2074\u20132083 (2018)","DOI":"10.1109\/CVPRW.2018.00281"},{"key":"27_CR19","doi-asserted-by":"crossref","unstructured":"Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: 300 faces in-the-wild challenge: the first facial landmark localization challenge. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 397\u2013403 (2013)","DOI":"10.1109\/ICCVW.2013.59"},{"key":"27_CR20","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1007\/978-3-319-97310-4_40","volume-title":"PRICAI 2018: Trends in Artificial Intelligence","author":"B Shao","year":"2018","unstructured":"Shao, B., Jiang, F., Shen, R.: Multi-object detection based on deep learning in real classrooms. In: Geng, X., Kang, B.-H. (eds.) PRICAI 2018. LNCS (LNAI), vol. 11013, pp. 352\u2013359. Springer, Cham (2018). \nhttps:\/\/doi.org\/10.1007\/978-3-319-97310-4_40"},{"key":"27_CR21","doi-asserted-by":"crossref","unstructured":"Shao, Z., Ding, S., Zhu, H., Wang, C., Ma, L.: Face alignment by deep convolutional network with adaptive learning rate. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1283\u20131287. IEEE (2016)","DOI":"10.1109\/ICASSP.2016.7471883"},{"key":"27_CR22","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint \narXiv:1409.1556\n\n (2014)"},{"key":"27_CR23","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. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"27_CR24","doi-asserted-by":"crossref","unstructured":"Voit, M., Stiefelhagen, R.: Deducing the visual focus of attention from head pose estimation in dynamic multi-view meeting scenarios. In: Proceedings of the 10th International Conference on Multimodal Interfaces, pp. 173\u2013180. ACM (2008)","DOI":"10.1145\/1452392.1452425"},{"key":"27_CR25","doi-asserted-by":"crossref","unstructured":"Yang, S., Luo, P., Loy, C.C., Tang, X.: Wider face: a face detection benchmark. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5525\u20135533 (2016)","DOI":"10.1109\/CVPR.2016.596"},{"issue":"10","key":"27_CR26","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499\u20131503 (2016)","journal-title":"IEEE Signal Process. Lett."},{"key":"27_CR27","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lei, Z., Liu, X., Shi, H., Li, S.Z.: Face alignment across large poses: a 3D solution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 146\u2013155 (2016)","DOI":"10.1109\/CVPR.2016.23"}],"container-title":["Lecture Notes in Computer Science","MultiMedia Modeling"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-37731-1_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,2,6]],"date-time":"2020-02-06T11:21:16Z","timestamp":1580988076000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-37731-1_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,24]]},"ISBN":["9783030377304","9783030377311"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-37731-1_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,24]]},"assertion":[{"value":"24 December 2019","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":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 January 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 January 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mmm2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.mmm2020.kr\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"171","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":"40","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":"23% - 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":"5","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Of the 171 submissions, 46 were accepted as poster papers; of the 49 special session paper submissions, 28 were accepted for oral presentation and 8 for poster presentation; 9 demo papers and 10 VBS papers were also accepted.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}