{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T05:06:41Z","timestamp":1743052001950,"version":"3.40.3"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031200731"},{"type":"electronic","value":"9783031200748"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20074-8_3","type":"book-chapter","created":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T20:23:11Z","timestamp":1668198191000},"page":"38-55","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["How to\u00a0Synthesize a\u00a0Large-Scale and\u00a0Trainable Micro-Expression Dataset?"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9061-6180","authenticated-orcid":false,"given":"Yuchi","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4483-8783","authenticated-orcid":false,"given":"Zhongdao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8356-4909","authenticated-orcid":false,"given":"Tom","family":"Gedeon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1464-9500","authenticated-orcid":false,"given":"Liang","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,12]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Baltrusaitis, T., Zadeh, A., Lim, Y.C., Morency, L.P.: Openface 2.0: facial behavior analysis toolkit. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 59\u201366. IEEE (2018)","DOI":"10.1109\/FG.2018.00019"},{"key":"3_CR2","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.patrec.2017.07.010","volume":"107","author":"X Ben","year":"2018","unstructured":"Ben, X., Jia, X., Yan, R., Zhang, X., Meng, W.: Learning effective binary descriptors for micro-expression recognition transferred by macro-information. Pattern Recogn. Lett. 107, 50\u201358 (2018)","journal-title":"Pattern Recogn. Lett."},{"key":"3_CR3","first-page":"5826","volume":"44","author":"X Ben","year":"2021","unstructured":"Ben, X., et al.: Video-based facial micro-expression analysis: a survey of datasets, features and algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 44, 5826\u20135846 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"10","key":"3_CR4","doi-asserted-by":"publisher","first-page":"119","DOI":"10.3390\/jimaging4100119","volume":"4","author":"A Davison","year":"2018","unstructured":"Davison, A., Merghani, W., Yap, M.: Objective classes for micro-facial expression recognition. J. Imaging 4(10), 119 (2018)","journal-title":"J. Imaging"},{"issue":"1","key":"3_CR5","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1109\/TAFFC.2016.2573832","volume":"9","author":"AK Davison","year":"2016","unstructured":"Davison, A.K., Lansley, C., Costen, N., Tan, K., Yap, M.H.: SAMM: a spontaneous micro-facial movement dataset. IEEE Trans. Affect. Comput. 9(1), 116\u2013129 (2016)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"3_CR6","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: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"15","key":"3_CR7","doi-asserted-by":"publisher","first-page":"E1454","DOI":"10.1073\/pnas.1322355111","volume":"111","author":"S Du","year":"2014","unstructured":"Du, S., Tao, Y., Martinez, A.M.: Compound facial expressions of emotion. Proc. Natl. Acad. Sci. 111(15), E1454\u2013E1462 (2014)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"3_CR8","unstructured":"Eckman, P., Friesen, W.: Facial action coding system (facs): a technique for the measurement of facial action. A8@ 5 3, 56\u201375 (1978)"},{"key":"3_CR9","volume-title":"What the face reveals: Basic and applied studies of spontaneous expression using the Facial Action Coding System (FACS)","author":"P Ekman","year":"1997","unstructured":"Ekman, P., Rosenberg, E.L.: What the face reveals: Basic and applied studies of spontaneous expression using the Facial Action Coding System (FACS). Oxford University Press, USA (1997)"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Fabian Benitez-Quiroz, C., Srinivasan, R., Martinez, A.M.: Emotionet: an accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5562\u20135570 (2016)","DOI":"10.1109\/CVPR.2016.600"},{"key":"3_CR11","series-title":"Lecture Notes in Electrical Engineering","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1007\/978-981-10-5041-1_68","volume-title":"Advanced Multimedia and Ubiquitous Engineering","author":"X Hao","year":"2017","unstructured":"Hao, X., Tian, M.: Deep belief network based on double weber local descriptor in micro-expression recognition. In: Park, J.J.J.H., Chen, S.-C., Raymond Choo, K.-K. (eds.) MUE\/FutureTech -2017. LNEE, vol. 448, pp. 419\u2013425. Springer, Singapore (2017). https:\/\/doi.org\/10.1007\/978-981-10-5041-1_68"},{"key":"3_CR12","unstructured":"Hoffman, J., et al.: Cycada: cycle-consistent adversarial domain adaptation. arXiv preprint arXiv:1711.03213 (2017)"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Huang, X., Wang, S.J., Zhao, G., Piteikainen, M.: Facial micro-expression recognition using spatiotemporal local binary pattern with integral projection. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 1\u20139 (2015)","DOI":"10.1109\/ICCVW.2015.10"},{"key":"3_CR14","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1016\/j.neucom.2015.10.096","volume":"175","author":"X Huang","year":"2016","unstructured":"Huang, X., Zhao, G., Hong, X., Zheng, W., Pietik\u00e4inen, M.: Spontaneous facial micro-expression analysis using spatiotemporal completed local quantized patterns. Neurocomputing 175, 564\u2013578 (2016)","journal-title":"Neurocomputing"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Kar, A., et al.: Meta-sim: learning to generate synthetic datasets. arXiv preprint arXiv:1904.11621 (2019)","DOI":"10.1109\/ICCV.2019.00465"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Khor, H.Q., See, J., Phan, R.C.W., Lin, W.: Enriched long-term recurrent convolutional network for facial micro-expression recognition. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 667\u2013674. IEEE (2018)","DOI":"10.1109\/FG.2018.00105"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Kim, D.H., Baddar, W.J., Ro, Y.M.: Micro-expression recognition with expression-state constrained spatio-temporal feature representations. In: Proceedings of the 24th ACM international conference on Multimedia, pp. 382\u2013386. ACM (2016)","DOI":"10.1145\/2964284.2967247"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Li, X., Pfister, T., Huang, X., Zhao, G., Pietik\u00e4inen, M.: A spontaneous micro-expression database: inducement, collection and baseline. In: 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1\u20136. IEEE (2013)","DOI":"10.1109\/FG.2013.6553717"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Li, Y., Huang, X., Zhao, G.: Can micro-expression be recognized based on single apex frame? In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 3094\u20133098. IEEE (2018)","DOI":"10.1109\/ICIP.2018.8451376"},{"key":"3_CR20","unstructured":"Liong, S.T., Gan, Y., Yau, W.C., Huang, Y.C., Ken, T.L.: Off-apexnet on micro-expression recognition system. arXiv preprint arXiv:1805.08699 (2018)"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Liu, Y., Du, H., Liang, Z., Gedeon, T.: A neural micro-expression recognizer. In: 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019). IEEE (2019)","DOI":"10.1109\/FG.2019.8756583"},{"key":"3_CR22","doi-asserted-by":"crossref","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. IEEE (2010)","DOI":"10.1109\/CVPRW.2010.5543262"},{"issue":"6","key":"3_CR23","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1037\/0022-3514.94.6.925","volume":"94","author":"D Matsumoto","year":"2008","unstructured":"Matsumoto, D., Yoo, S.H., Nakagawa, S.: Culture, emotion regulation, and adjustment. J. Pers. Soc. Psychol. 94(6), 925 (2008)","journal-title":"J. Pers. Soc. Psychol."},{"key":"3_CR24","unstructured":"Patel, D., Hong, X., Zhao, G.: Selective deep features for micro-expression recognition. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2258\u20132263. IEEE (2016)"},{"key":"3_CR25","doi-asserted-by":"publisher","first-page":"1745","DOI":"10.3389\/fpsyg.2017.01745","volume":"8","author":"M Peng","year":"2017","unstructured":"Peng, M., Wang, C., Chen, T., Liu, G., Fu, X.: Dual temporal scale convolutional neural network for micro-expression recognition. Front. Psychol. 8, 1745 (2017)","journal-title":"Front. Psychol."},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Peng, M., Wu, Z., Zhang, Z., Chen, T.: From macro to micro expression recognition: Deep learning on small datasets using transfer learning. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 657\u2013661. IEEE (2018)","DOI":"10.1109\/FG.2018.00103"},{"key":"3_CR27","doi-asserted-by":"crossref","unstructured":"Polikovsky, S., Kameda, Y., Ohta, Y.: Facial micro-expressions recognition using high speed camera and 3d-gradient descriptor (2009)","DOI":"10.1049\/ic.2009.0244"},{"issue":"1","key":"3_CR28","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1587\/transinf.E96.D.81","volume":"96","author":"S Polikovsky","year":"2013","unstructured":"Polikovsky, S., Kameda, Y., Ohta, Y.: Facial micro-expression detection in hi-speed video based on facial action coding system (FACS). IEICE Trans. Inf. Syst. 96(1), 81\u201392 (2013)","journal-title":"IEICE Trans. Inf. Syst."},{"key":"3_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1007\/978-3-030-01249-6_50","volume-title":"Computer Vision \u2013 ECCV 2018","author":"A Pumarola","year":"2018","unstructured":"Pumarola, A., Agudo, A., Martinez, A.M., Sanfeliu, A., Moreno-Noguer, F.: GANimation: anatomically-aware facial animation from a single image. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 835\u2013851. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01249-6_50"},{"key":"3_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1007\/978-3-319-46475-6_7","volume-title":"Computer Vision \u2013 ECCV 2016","author":"SR Richter","year":"2016","unstructured":"Richter, S.R., Vineet, V., Roth, S., Koltun, V.: Playing for data: ground truth from computer games. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 102\u2013118. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_7"},{"key":"3_CR31","unstructured":"Ruiz, N., Schulter, S., Chandraker, M.: Learning to simulate. arXiv preprint arXiv:1810.02513 (2018)"},{"issue":"9","key":"3_CR32","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1007\/s11263-018-1072-8","volume":"126","author":"C Sakaridis","year":"2018","unstructured":"Sakaridis, C., Dai, D., Van Gool, L.: Semantic foggy scene understanding with synthetic data. Int. J. Comput. Vision 126(9), 973\u2013992 (2018)","journal-title":"Int. J. Comput. Vision"},{"key":"3_CR33","doi-asserted-by":"crossref","unstructured":"See, J., Yap, M.H., Li, J., Hong, X., Wang, S.J.: Megc 2019-the second facial micro-expressions grand challenge. In: 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), pp. 1\u20135. IEEE (2019)","DOI":"10.1109\/FG.2019.8756611"},{"key":"3_CR34","doi-asserted-by":"crossref","unstructured":"Shreve, M., Godavarthy, S., Goldgof, D., Sarkar, S.: Macro-and micro-expression spotting in long videos using spatio-temporal strain. In: Face and Gesture 2011, pp. 51\u201356. IEEE (2011)","DOI":"10.1109\/FG.2011.5771451"},{"key":"3_CR35","doi-asserted-by":"crossref","unstructured":"Tremblay, J., et al.: Training deep networks with synthetic data: Bridging the reality gap by domain randomization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 969\u2013977 (2018)","DOI":"10.1109\/CVPRW.2018.00143"},{"key":"3_CR36","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.neucom.2018.05.107","volume":"312","author":"SJ Wang","year":"2018","unstructured":"Wang, S.J., et al.: Micro-expression recognition with small sample size by transferring long-term convolutional neural network. Neurocomputing 312, 251\u2013262 (2018)","journal-title":"Neurocomputing"},{"key":"3_CR37","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/978-3-319-16865-4_34","volume-title":"Computer Vision \u2013 ACCV 2014","author":"Y Wang","year":"2015","unstructured":"Wang, Y., See, J., Phan, R.C.-W., Oh, Y.-H.: LBP with six intersection points: reducing redundant information in LBP-TOP for micro-expression recognition. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9003, pp. 525\u2013537. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-16865-4_34"},{"issue":"2","key":"3_CR38","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1109\/TAFFC.2016.2518162","volume":"8","author":"F Xu","year":"2017","unstructured":"Xu, F., Zhang, J., Wang, J.Z.: Microexpression identification and categorization using a facial dynamics map. IEEE Trans. Affect. Comput. 8(2), 254\u2013267 (2017)","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"1","key":"3_CR39","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0086041","volume":"9","author":"WJ Yan","year":"2014","unstructured":"Yan, W.J., et al.: Casme II: an improved spontaneous micro-expression database and the baseline evaluation. PLoS ONE 9(1), e86041 (2014)","journal-title":"PLoS ONE"},{"key":"3_CR40","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1007\/978-3-030-58539-6_46","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Yao","year":"2020","unstructured":"Yao, Y., Zheng, L., Yang, X., Naphade, M., Gedeon, T.: Simulating content consistent vehicle datasets with attribute descent. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12351, pp. 775\u2013791. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58539-6_46"},{"issue":"10","key":"3_CR41","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). https:\/\/doi.org\/10.1109\/LSP.2016.2603342","journal-title":"IEEE Signal Process. Lett."},{"issue":"6","key":"3_CR42","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1109\/TPAMI.2007.1110","volume":"29","author":"G Zhao","year":"2007","unstructured":"Zhao, G., Pietikainen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915\u2013928 (2007)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR43","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3754\u20133762 (2017)","DOI":"10.1109\/ICCV.2017.405"},{"key":"3_CR44","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, L., Zheng, Z., Li, S., Yang, Y.: Camera style adaptation for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5157\u20135166 (2018)","DOI":"10.1109\/CVPR.2018.00541"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20074-8_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T20:23:34Z","timestamp":1668198214000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20074-8_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200731","9783031200748"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20074-8_3","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":"12 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","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":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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)"}}]}}