{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:35:23Z","timestamp":1742913323460,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030963071"},{"type":"electronic","value":"9783030963088"}],"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\/978-3-030-96308-8_2","type":"book-chapter","created":{"date-parts":[[2022,3,26]],"date-time":"2022-03-26T13:15:41Z","timestamp":1648300541000},"page":"13-22","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Face Mask Detection: Prevention and\u00a0Mitigation of\u00a0COVID-19"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1812-8355","authenticated-orcid":false,"given":"Sahar","family":"Dammak","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0285-0944","authenticated-orcid":false,"given":"Hazar","family":"Mliki","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2328-2616","authenticated-orcid":false,"given":"Emna","family":"Fendri","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,27]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","first-page":"170116","DOI":"10.1109\/ACCESS.2019.2955383","volume":"7","author":"H Chen","year":"2019","unstructured":"Chen, H., Chen, Y., Tian, X., Jiang, R.: A cascade face spoofing detector based on face anti-spoofing R-CNN and improved Retinex LBP. IEEE Access 7, 170116\u2013170133 (2019)","journal-title":"IEEE Access"},{"key":"2_CR2","unstructured":"Daniell, C.: Detect faces and determine whether people are wearing mask (2020). https:\/\/github.com\/AIZOOTech\/FaceMaskDetection"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Zhou, Y., Yu, J., Kotsia, I., Zafeiriou, S.: RetinaFace: single-stage dense face localisation in the wild. arXiv preprint arXiv:1905.00641 (2019)","DOI":"10.1109\/CVPR42600.2020.00525"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Ge, S., Li, J., Ye, Q., Luo, Z.: Detecting masked faces in the wild with LLE-CNNs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2682\u20132690 (2017)","DOI":"10.1109\/CVPR.2017.53"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"10223","key":"2_CR6","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","volume":"395","author":"C Huang","year":"2020","unstructured":"Huang, C., et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. lancet 395(10223), 497\u2013506 (2020)","journal-title":"lancet"},{"key":"2_CR7","unstructured":"Jiang, M., Fan, X.: RetinaMask: a face mask detector. arXiv preprint arXiv:2005.03950 (2020)"},{"key":"2_CR8","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1016\/j.procs.2020.06.114","volume":"174","author":"Q Jin","year":"2020","unstructured":"Jin, Q., Mu, C., Tian, L., Ran, F.: A region generation based model for occluded face detection. Procedia Comput. Sci. 174, 454\u2013462 (2020)","journal-title":"Procedia Comput. Sci."},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Joshi, A.S., Joshi, S.S., Kanahasabai, G., Kapil, R., Gupta, S.: Deep learning framework to detect face masks from video footage. In: 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN), pp. 435\u2013440. IEEE (2020)","DOI":"10.1109\/CICN49253.2020.9242625"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Li, H., Alghowinem, S., Caldwell, S., Gedeon, T.: Interpretation of occluded face detection using convolutional neural network. In: 2019 IEEE 23rd International Conference on Intelligent Engineering Systems (INES), pp. 000165\u2013000170. IEEE (2019)","DOI":"10.1109\/INES46365.2019.9109524"},{"key":"2_CR11","doi-asserted-by":"publisher","first-page":"108288","DOI":"10.1016\/j.measurement.2020.108288","volume":"167","author":"M Loey","year":"2021","unstructured":"Loey, M., Manogaran, G., Taha, M.H.N., Khalifa, N.E.M.: A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic. Measurement 167, 108288 (2021)","journal-title":"Measurement"},{"issue":"7","key":"2_CR12","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1007\/s11760-020-01680-w","volume":"14","author":"H Mliki","year":"2020","unstructured":"Mliki, H., Dammak, S., Fendri, E.: An improved multi-scale face detection using convolutional neural network. Signal Image Video Process. 14(7), 1345\u20131353 (2020). https:\/\/doi.org\/10.1007\/s11760-020-01680-w","journal-title":"Signal Image Video Process."},{"key":"2_CR13","unstructured":"Prajnasb: observations (2020). https:\/\/github.com\/prajnasb\/observations"},{"key":"2_CR14","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/978-3-030-73280-6_40","volume-title":"Intelligent Information and Database Systems","author":"MD Putro","year":"2021","unstructured":"Putro, M.D., Nguyen, D.-L., Jo, K.-H.: Real-time multi-view face mask detector on edge device for supporting service robots in the COVID-19 pandemic. In: Nguyen, N.T., Chittayasothorn, S., Niyato, D., Trawi\u0144ski, B. (eds.) ACIIDS 2021. LNCS (LNAI), vol. 12672, pp. 507\u2013517. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-73280-6_40"},{"key":"2_CR15","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":"2_CR16","unstructured":"Ruder, S.: An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)"},{"issue":"1","key":"2_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1\u201348 (2019)","journal-title":"J. Big Data"},{"key":"2_CR18","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Snyder, S.E., Husari, G.: Thor: a deep learning approach for face mask detection to prevent the COVID-19 pandemic. In: SoutheastCon 2021, pp. 1\u20138. IEEE (2021)","DOI":"10.1109\/SoutheastCon45413.2021.9401874"},{"key":"2_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/978-3-030-03338-5_18","volume-title":"Pattern Recognition and Computer Vision","author":"Y Su","year":"2018","unstructured":"Su, Y., Wan, X., Guo, Z.: Robust face detector with fully convolutional networks. In: Lai, J.-H., et al. (eds.) PRCV 2018. LNCS, vol. 11258, pp. 207\u2013218. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-03338-5_18"},{"key":"2_CR21","unstructured":"WHO: World Health Organization: Who characterizes COVID-19 as a pandemic (2020). https:\/\/www.who.int\/dg\/speeches\/detail\/who-director-general-s-opening-remarks-at-the-mediabriefing-on-covid-19-11-march-2020"},{"issue":"11","key":"2_CR22","doi-asserted-by":"publisher","first-page":"4017","DOI":"10.1109\/TCYB.2018.2859482","volume":"49","author":"W Wu","year":"2018","unstructured":"Wu, W., Yin, Y., Wang, X., Xu, D.: Face detection with different scales based on faster R-CNN. IEEE Trans. Cybern. 49(11), 4017\u20134028 (2018)","journal-title":"IEEE Trans. Cybern."},{"key":"2_CR23","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"}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Systems Design and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-96308-8_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,26]],"date-time":"2022-03-26T13:18:07Z","timestamp":1648300687000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-96308-8_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030963071","9783030963088"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-96308-8_2","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"27 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISDA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Systems Design and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isda2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.mirlabs.net\/isda21\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}