{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T05:36:45Z","timestamp":1742967405245,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819980758"},{"type":"electronic","value":"9789819980765"}],"license":[{"start":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T00:00:00Z","timestamp":1699920000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T00:00:00Z","timestamp":1699920000000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8076-5_13","type":"book-chapter","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T14:02:10Z","timestamp":1699884130000},"page":"179-190","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Micro-expression Recognition Based on\u00a0PCB-PCANet+"],"prefix":"10.1007","author":[{"given":"Shiqi","family":"Wang","sequence":"first","affiliation":[]},{"given":"Fei","family":"Long","sequence":"additional","affiliation":[]},{"given":"Junfeng","family":"Yao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,14]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Pfister, T., Li, X., Zhao, G., Pietik\u00e4inen, M.: Recognising spontaneous facial micro-expressions. In: Proceedings of International Conference on Computer Vision, pp. 1449\u20131456 (2011)","DOI":"10.1109\/ICCV.2011.6126401"},{"issue":"6","key":"13_CR2","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":"13_CR3","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: Proceedings of 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, pp. 1\u20136 (2013)","DOI":"10.1109\/FG.2013.6553717"},{"key":"13_CR4","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":"11","key":"13_CR5","doi-asserted-by":"publisher","first-page":"3160","DOI":"10.1109\/TMM.2018.2820321","volume":"20","author":"Y Zong","year":"2018","unstructured":"Zong, Y., Huang, X., Zheng, W., Cui, Z., Zhao, G.: Learning from hierarchical spatiotemporal descriptors for micro-expression recognition. IEEE Trans. Multimedia 20(11), 3160\u20133172 (2018)","journal-title":"IEEE Trans. Multimedia"},{"key":"13_CR6","doi-asserted-by":"publisher","first-page":"1618","DOI":"10.1109\/TIP.2019.2912358","volume":"29","author":"M Verma","year":"2020","unstructured":"Verma, M., Vipparthi, S.K., Singh, G., Murala, S.: Learnet: dynamic imaging network for micro expression recognition. IEEE Trans. Image Process. 29, 1618\u20131627 (2020)","journal-title":"IEEE Trans. Image Process."},{"issue":"12","key":"13_CR7","doi-asserted-by":"publisher","first-page":"2799","DOI":"10.1109\/TPAMI.2017.2769085","volume":"40","author":"H Bilen","year":"2018","unstructured":"Bilen, H., Fernando, B., Gavves, E., Vedaldi, A.: Action recognition with dynamic image networks. IEEE Trans. Pattern Anal. Mach. Intell. 40(12), 2799\u20132813 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"13_CR8","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.ins.2022.11.113","volume":"630","author":"S Thuseethan","year":"2023","unstructured":"Thuseethan, S., Rajasegarar, S., Yearwood, J.: Deep3DCANN: a deep 3DCNN-ANN framework for spontaneous micro-expression recognition. Inf. Sci. 630, 341\u2013355 (2023)","journal-title":"Inf. Sci."},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Xu, S., Zhou, Z., Shang, J.: Asymmetric adversarial-based feature disentanglement learning for cross-database micro-expression recognition. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 5342\u20135350 (2022)","DOI":"10.1145\/3503161.3548435"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Mayya, V., Pai, R.M., Manohara Pai, M.: Combining temporal interpolation and DCNN for faster recognition of micro-expressions in video sequences. In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 699\u2013703 (2016)","DOI":"10.1109\/ICACCI.2016.7732128"},{"issue":"6","key":"13_CR11","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017)","journal-title":"Commun. ACM"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Xia, B., Wang, S.: Micro-expression recognition enhanced by macro-expression from spatial-temporal domain. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), pp. 1186\u20131193 (2021)","DOI":"10.24963\/ijcai.2021\/164"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Sun, Y., Zheng, L., Yang, Y., Tian, Q., Wang, S.: Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). In: Proceedings of the European conference on computer vision (ECCV), pp. 480\u2013496 (2018)","DOI":"10.1007\/978-3-030-01225-0_30"},{"issue":"1","key":"13_CR14","doi-asserted-by":"publisher","first-page":"1","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), 1\u20138 (2014)","journal-title":"PLoS ONE"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Huang, X., Wang, S., Zhao, G., Piteikainen, M.: Facial micro-expression recognition using spatiotemporal local binary pattern with integral projection. In: Proceedings of IEEE International Conference on Computer Vision Works, Los Alamitos, CA, USA, pp. 1\u20139 (2015)","DOI":"10.1109\/ICCVW.2015.10"},{"key":"13_CR16","doi-asserted-by":"crossref","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 (2016)","DOI":"10.1109\/ICPR.2016.7899972"},{"issue":"4","key":"13_CR17","doi-asserted-by":"publisher","first-page":"1331","DOI":"10.1007\/s10044-018-0757-5","volume":"22","author":"J Li","year":"2019","unstructured":"Li, J., Wang, Y., See, J., Liu, W.: Micro-expression recognition based on 3d flow convolutional neural network. Pattern Analysis and Applications 22(4), 1331\u20131339 (2019)","journal-title":"Pattern Analysis and Applications"},{"key":"13_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108275","volume":"122","author":"L Zhou","year":"2022","unstructured":"Zhou, L., Mao, Q., Huang, X., Zhang, F., Zhang, Z.: Feature refinement: an expression-specific feature learning and fusion method for micro-expression recognition. Pattern Recogn. 122, 108275 (2022)","journal-title":"Pattern Recogn."},{"issue":"11","key":"13_CR19","doi-asserted-by":"publisher","first-page":"4296","DOI":"10.3390\/s22114296","volume":"22","author":"S Wang","year":"2022","unstructured":"Wang, S., Guan, S., Lin, H., Huang, J., Long, F., Yao, J.: Micro-expression recognition based on optical flow and pcanet+. Sensors 22(11), 4296 (2022)","journal-title":"Sensors"},{"key":"13_CR20","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 (2018)","DOI":"10.1109\/FG.2018.00105"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8076-5_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T22:29:31Z","timestamp":1730500171000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8076-5_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,14]]},"ISBN":["9789819980758","9789819980765"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8076-5_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,11,14]]},"assertion":[{"value":"14 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","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":"650","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":"51% - 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":"4.14","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":"2.46","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)"}}]}}