{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T20:26:39Z","timestamp":1744403199569,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030057091"},{"type":"electronic","value":"9783030057107"}],"license":[{"start":{"date-parts":[[2018,12,8]],"date-time":"2018-12-08T00:00:00Z","timestamp":1544227200000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-05710-7_24","type":"book-chapter","created":{"date-parts":[[2018,12,7]],"date-time":"2018-12-07T12:48:55Z","timestamp":1544186935000},"page":"289-299","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Incremental Training for Face Recognition"],"prefix":"10.1007","author":[{"given":"Martin","family":"Winter","sequence":"first","affiliation":[]},{"given":"Werner","family":"Bailer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,12,8]]},"reference":[{"key":"24_CR1","unstructured":"Amos, B., Ludwiczuk, B., Satyanarayanan, M., et al.: OpenFace: a general-purpose face recognition library with mobile applications. Technical Report CMU-CS-16-118, CMU School of Computer Science (2016)"},{"key":"24_CR2","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breimann","year":"2001","unstructured":"Breimann, L.: Random forests. Mach. Learn. 45, 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"24_CR3","doi-asserted-by":"crossref","unstructured":"Chen, S., Liu, Y., Gao, X., Han, B.: MobileFaceNets: efficient CNNs for accurate real-time face verification on mobile devices. In: Chinese Conference on Biometric Recognition (2018)","DOI":"10.1007\/978-3-319-97909-0_46"},{"issue":"8","key":"24_CR4","doi-asserted-by":"publisher","first-page":"2868","DOI":"10.1016\/j.patcog.2012.02.002","volume":"45","author":"K Choi","year":"2012","unstructured":"Choi, K., Toh, K.-A., Byun, H.: Incremental face recognition for large-scale social network services. Pattern Recognit. 45(8), 2868\u20132883 (2012)","journal-title":"Pattern Recognit."},{"key":"24_CR5","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1561\/0600000035","volume":"7","author":"A Criminisi","year":"2012","unstructured":"Criminisi, A., Shotton, J., Konukoglu, E.: Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Found. Trends Comput. Graph. Vis. 7, 81\u2013227 (2012)","journal-title":"Found. Trends Comput. Graph. Vis."},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Zafeiriou, S.: Arcface: additive angular margin loss for deep face recognition. CoRR, abs\/1801.07698 (2018)","DOI":"10.1109\/CVPR.2019.00482"},{"key":"24_CR7","doi-asserted-by":"crossref","unstructured":"Farfade, S.S., Saberian, M.J., Li, L.-J.: Multi-view face detection using deep convolutional neural networks. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp. 643\u2013650. ACM (2015)","DOI":"10.1145\/2671188.2749408"},{"issue":"1","key":"24_CR8","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst.Sci. 55(1), 119\u2013139 (1997)","journal-title":"J. Comput. Syst.Sci."},{"issue":"5","key":"24_CR9","first-page":"771","volume":"14","author":"Y Freund","year":"1999","unstructured":"Freund, Y., Schapire, R.E.: A short introduction to boosting. J. Jpn. Soc. Artif. Intell. 14(5), 771\u2013780 (1999). English translation","journal-title":"J. Jpn. Soc. Artif. Intell."},{"issue":"11","key":"24_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2352\/ISSN.2470-1173.2016.11.IMAWM-463","volume":"2016","author":"Y Guo","year":"2016","unstructured":"Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: MS-Celeb-1M: challenge of recognizing one million celebrities in the real world. Electron. Imaging 2016(11), 1\u20136 (2016)","journal-title":"Electron. Imaging"},{"key":"24_CR11","doi-asserted-by":"crossref","unstructured":"Jiang, H., Learned-Miller, E.: Face detection with the faster R-CNN. In: 2017 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017, pp. 650\u2013657. IEEE (2017)","DOI":"10.1109\/FG.2017.82"},{"issue":"Jul","key":"24_CR12","first-page":"1755","volume":"10","author":"DE King","year":"2009","unstructured":"King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10(Jul), 1755\u20131758 (2009)","journal-title":"J. Mach. Learn. Res."},{"key":"24_CR13","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/978-3-319-25958-1_8","volume-title":"Advances in Face Detection and Facial Image Analysis","author":"E Learned-Miller","year":"2016","unstructured":"Learned-Miller, E., Huang, G.B., RoyChowdhury, A., Li, H., Hua, G.: Labeled faces in the wild: a survey. In: Kawulok, M., Celebi, M.E., Smolka, B. (eds.) Advances in Face Detection and Facial Image Analysis, pp. 189\u2013248. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-25958-1_8"},{"key":"24_CR14","doi-asserted-by":"crossref","unstructured":"Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G.: A convolutional neural network cascade for face detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5325\u20135334 (2015)","DOI":"10.1109\/CVPR.2015.7299170"},{"key":"24_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1007\/978-3-319-10593-2_47","volume-title":"Computer Vision","author":"M Mathias","year":"2014","unstructured":"Mathias, M., Benenson, R., Pedersoli, M., Van Gool, L.: Face detection without bells and whistles. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 720\u2013735. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10593-2_47"},{"key":"24_CR16","unstructured":"Oza, N.C., Russell, S.: Online bagging and boosting. In: Eighth International Workshop on Artificial Intelligence and Statistics, pp. 105\u2013112 (2001)"},{"issue":"5\u20136","key":"24_CR17","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1016\/j.neunet.2005.06.016","volume":"18","author":"S Ozawa","year":"2005","unstructured":"Ozawa, S., Toh, S.L., Abe, S., Pang, S., Kasabov, N.: Incremental learning of feature space and classifier for face recognition. Neural Netw. 18(5\u20136), 575\u2013584 (2005)","journal-title":"Neural Netw."},{"key":"24_CR18","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. (2017)"},{"issue":"1","key":"24_CR19","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1109\/34.655647","volume":"20","author":"HA Rowley","year":"1998","unstructured":"Rowley, H.A., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 23\u201338 (1998)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Saffari, A., Leistner, C., Santner, J., Godec, M., Bischof, H.: On-line random forests. In: 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pages 1393\u20131400. IEEE (2009)","DOI":"10.1109\/ICCVW.2009.5457447"},{"key":"24_CR21","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815\u2013823 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2892\u20132900 (2015)","DOI":"10.1109\/CVPR.2015.7298907"},{"key":"24_CR23","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V.: Inception-v4, inception-resnet and the impact of residual connections on learning. CoRR, abs\/1602.07261 (2016)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"24_CR24","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Deepface: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701\u20131708 (2014)","DOI":"10.1109\/CVPR.2014.220"},{"key":"24_CR25","volume-title":"The Nature of Statistical Learning Theory","author":"V Vapnik","year":"2013","unstructured":"Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (2013)"},{"issue":"2","key":"24_CR26","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","volume":"57","author":"P Viola","year":"2004","unstructured":"Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137\u2013154 (2004)","journal-title":"Int. J. Comput. Vis."},{"issue":"4","key":"24_CR27","doi-asserted-by":"publisher","first-page":"940","DOI":"10.1109\/TSMCB.2010.2101591","volume":"41","author":"YW Wong","year":"2011","unstructured":"Wong, Y.W., Seng, K.P., Ang, L.M.: Radial basis function neural network with incremental learning for face recognition. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 41(4), 940\u2013949 (2011)","journal-title":"IEEE Trans. Syst. Man Cybern. Part B (Cybern.)"},{"key":"24_CR28","doi-asserted-by":"crossref","unstructured":"Yan, J., Lei, Z., Wen, L., Li, S.Z.: The fastest deformable part model for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2497\u20132504 (2014)","DOI":"10.1109\/CVPR.2014.320"},{"issue":"10","key":"24_CR29","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":"24_CR30","doi-asserted-by":"crossref","unstructured":"Zhu, C., Zheng, Y., Luu, K., Savvides, M.: CMS-RCNN: contextual multi-scale region-based CNN for unconstrained face detection. arXiv preprint arXiv:1606.05413 (2016)","DOI":"10.1007\/978-3-319-61657-5_3"},{"key":"24_CR31","unstructured":"Zhu, Z., Luo, P., Wang, X., Tang, X.: Recover canonical-view faces in the wild with deep neural networks. arXiv preprint arXiv:1404.3543 (2014)"}],"container-title":["Lecture Notes in Computer Science","MultiMedia Modeling"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-05710-7_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T21:17:19Z","timestamp":1662585439000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-05710-7_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,8]]},"ISBN":["9783030057091","9783030057107"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-05710-7_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018,12,8]]},"assertion":[{"value":"8 December 2018","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":"Thessaloniki","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 January 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 January 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mmm2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/mmm2019.iti.gr\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double blind for full papers and workshop papers, single blind for other paper types","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"204","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"96","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"47% - 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"}},{"value":"2.67","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"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"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"6 demonstration papers, 5 industry papers, 6 workshop papers, and 6 Video Browser Showdown 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"}}]}}