{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T03:26:46Z","timestamp":1752550006178,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031217555"},{"type":"electronic","value":"9783031217562"}],"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\/978-3-031-21756-2_8","type":"book-chapter","created":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T04:39:27Z","timestamp":1670301567000},"page":"101-111","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Impact Analysis of\u00a0Different Effective Loss Functions by\u00a0Using Deep Convolutional Neural Network for\u00a0Face Recognition"],"prefix":"10.1007","author":[{"given":"Anh D.","family":"Nguyen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dat T.","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hai N.","family":"Dao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hai H.","family":"Le","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nam Q.","family":"Tran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,7]]},"reference":[{"key":"8_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 (2018)","DOI":"10.1109\/FG.2018.00019"},{"key":"8_CR2","doi-asserted-by":"crossref","unstructured":"Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: VGGFace2: a dataset for recognising faces across pose and age. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 67\u201374 (2018)","DOI":"10.1109\/FG.2018.00020"},{"key":"8_CR3","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4685\u20134694 (2019)","DOI":"10.1109\/CVPR.2019.00482"},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"Du, H.P., Pham, D.H., Nguyen, H.N.: An efficient parallel method for optimizing concurrent operations on social networks. Trans. Comput. Collective Intell. 10840(XXIX), 182\u2013199 (2018)","DOI":"10.1007\/978-3-319-90287-6_10"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: Ms-celeb-1m: a dataset and benchmark for large-scale face recognition. In: ECCV (2016)","DOI":"10.1007\/978-3-319-46487-9_6"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"8_CR7","unstructured":"Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical Report 07\u201349, University of Massachusetts, Amherst (2007)"},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Huang, X., Du, X., Liu, H., Zang, W.: A research on face recognition open source development framework based on PyTorch. In: 2021 International Symposium on Computer Technology and Information Science (ISCTIS), pp. 346\u2013350 (2021)","DOI":"10.1109\/ISCTIS51085.2021.00077"},{"issue":"17","key":"8_CR9","doi-asserted-by":"publisher","first-page":"25741","DOI":"10.1007\/s11042-021-10865-5","volume":"80","author":"J Jiao","year":"2021","unstructured":"Jiao, J., Liu, W., Mo, Y., Jiao, J., Deng, Z., Chen, X.: Dyn-ArcFace: dynamic additive angular margin loss for deep face recognition. Multimedia Tools Appl. 80(17), 25741\u201325756 (2021)","journal-title":"Multimedia Tools Appl."},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Kemelmacher-Shlizerman, I., Seitz, S.M., Miller, D., Brossard, E.: The megaface benchmark: 1 million faces for recognition at scale. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4873\u20134882 (2016)","DOI":"10.1109\/CVPR.2016.527"},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Le, H.V., Nguyen, T.N., Nguyen, H.N., Le, L.: An efficient hybrid webshell detection method for webserver of marine transportation systems. IEEE Trans. Intell. Transp. Syst. Early Access, 1\u201313 (2021)","DOI":"10.1109\/TITS.2021.3122979"},{"key":"8_CR12","unstructured":"Liu, W., Wen, Y., Yu, Z., Yang, M.: Large-margin softmax loss for convolutional neural networks. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48, ICML\u201916, pp. 507\u2013516 (2016)"},{"key":"8_CR13","doi-asserted-by":"crossref","unstructured":"Meng, Q., Zhao, S., Huang, Z., Zhou, F.: MagFace: a universal representation for face recognition and quality assessment. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14220\u201314229 (2021)","DOI":"10.1109\/CVPR46437.2021.01400"},{"key":"8_CR14","doi-asserted-by":"crossref","unstructured":"Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., Zafeiriou, S: AgeDB: the first manually collected, in-the-wild age database. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1997\u20132005 (2017)","DOI":"10.1109\/CVPRW.2017.250"},{"key":"8_CR15","unstructured":"Ranjan, R., Castillo, C., Chellappa, R.: L2-constrained softmax loss for discriminative face verification. CoRR, abs\/1703.09507 (2017)"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 815\u2013823 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Sengupta, S., Chen, J.-C., Castillo, C., Patel, V.M., Chellappa, R., Jacobs, D.W.: Frontal to profile face verification in the wild. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1\u20139 (2016)","DOI":"10.1109\/WACV.2016.7477558"},{"key":"8_CR18","doi-asserted-by":"crossref","unstructured":"Tao, K., He, Y., Chen, C.: Design of face recognition system based on convolutional neural network. In: 2019 Chinese Automation Congress (CAC), pp. 5403\u20135406 (2019)","DOI":"10.1109\/CAC48633.2019.8996236"},{"issue":"7","key":"8_CR19","doi-asserted-by":"publisher","first-page":"926","DOI":"10.1109\/LSP.2018.2822810","volume":"25","author":"F Wang","year":"2018","unstructured":"Wang, F., Cheng, J., Liu, W., Liu, H.: Additive margin softmax for face verification. IEEE Signal Process. Lett. 25(7), 926\u2013930 (2018)","journal-title":"IEEE Signal Process. Lett."},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: CosFace: large margin cosine loss for deep face recognition. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5265\u20135274 (2018)","DOI":"10.1109\/CVPR.2018.00552"},{"key":"8_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1007\/978-3-319-46478-7_31","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Y Wen","year":"2016","unstructured":"Wen, Y., Zhang, K., Li, Z., Qiao, Yu.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499\u2013515. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_31"},{"key":"8_CR22","unstructured":"Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch (2014)"},{"key":"8_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhao, R., Qiao, Y., Wang, X., Li, H.: AdaCos: adaptively scaling cosine logits for effectively learning deep face representations. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10815\u201310824 (2019)","DOI":"10.1109\/CVPR.2019.01108"},{"key":"8_CR24","unstructured":"Zheng, T., Deng, W.: Cross-pose LFW: a database for studying cross-pose face recognition in unconstrained environments. Technical Report 18\u201301, Beijing University of Posts and Telecommunications (2018)"},{"key":"8_CR25","unstructured":"Zheng, T., Deng, W., Hu, J.: Cross-age LFW: a database for studying cross-age face recognition in unconstrained environments. CoRR, abs\/1708.08197 (2017)"},{"key":"8_CR26","doi-asserted-by":"crossref","unstructured":"Zhu, Z., et al.: Webface260m: a benchmark unveiling the power of million-scale deep face recognition. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10487\u201310497 (2021)","DOI":"10.1109\/CVPR46437.2021.01035"}],"container-title":["Lecture Notes in Computer Science","From Born-Physical to Born-Virtual: Augmenting Intelligence in Digital Libraries"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21756-2_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T04:41:57Z","timestamp":1670301717000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21756-2_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031217555","9783031217562"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21756-2_8","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":"7 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICADL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Asian Digital Libraries","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanoi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","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":"30 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icadl2022","order":10,"name":"conference_id","label":"Conference ID","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":"78","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":"14","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":"18","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":"18% - 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":"3.08","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":"4.07","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)"}}]}}