{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T08:33:11Z","timestamp":1770971591325,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T00:00:00Z","timestamp":1658448000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T00:00:00Z","timestamp":1658448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Karlsruher Institut f\u00fcr Technologie (KIT)"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Health organizations advise social distancing, wearing face mask, and avoiding touching face to prevent the spread of coronavirus. Based on these protective measures, we developed a computer vision system to help prevent the transmission of COVID-19. Specifically, the developed system performs face mask detection, face-hand interaction detection, and measures social distance. To train and evaluate the developed system, we collected and annotated images that represent face mask usage and face-hand interaction in the real world. Besides assessing the performance of the developed system on our own datasets, we also tested it on existing datasets in the literature without performing any adaptation on them. In addition, we proposed a module to track social distance between people. Experimental results indicate that our datasets represent the real-world\u2019s diversity well. The proposed system achieved very high performance and generalization capacity for face mask usage detection, face-hand interaction detection, and measuring social distance in a real-world scenario on unseen data. The datasets are available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/iremeyiokur\/COVID-19-Preventions-Control-System\">https:\/\/github.com\/iremeyiokur\/COVID-19-Preventions-Control-System<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/s11760-022-02308-x","type":"journal-article","created":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T19:45:22Z","timestamp":1658519122000},"page":"1027-1034","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Unconstrained face mask and face-hand interaction datasets: building a computer vision system to help prevent the transmission of COVID-19"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5754-5405","authenticated-orcid":false,"given":"Fevziye Irem","family":"Eyiokur","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haz\u0131m Kemal","family":"Ekenel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Waibel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,22]]},"reference":[{"key":"2308_CR1","unstructured":"Coronavirus disease advice for the public. https:\/\/www.who.int\/emergencies\/diseases\/novel-coronavirus-2019\/advice-for-public. Accessed: 2021-05-01"},{"key":"2308_CR2","unstructured":"Covid-19: physical distancing. https:\/\/www.who.int\/westernpacific\/emergencies\/covid-19\/information\/physical-distancing. Accessed: 2021-05-01"},{"issue":"3","key":"2308_CR3","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1109\/29.21701","volume":"37","author":"A Waibel","year":"1989","unstructured":"Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., Lang, K.J.: Phoneme recognition using time-delay neural networks. IEEE Trans. Acoust. Speech Signal Process. 37(3), 328\u2013339 (1989)","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"2308_CR4","unstructured":"Le\u00a0Cun, Y., et\u00a0al.: Handwritten digit recognition with a back-propagation network. In: NeurIPS (1989)"},{"key":"2308_CR5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"J Chen","year":"2020","unstructured":"Chen, J., et al.: Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography. Sci. Rep. 10, 1\u201311 (2020)","journal-title":"Sci. Rep."},{"issue":"2","key":"2308_CR6","doi-asserted-by":"publisher","first-page":"E65","DOI":"10.1148\/radiol.2020200905","volume":"296","author":"L Li","year":"2020","unstructured":"Li, L., et al.: Using artificial intelligence to detect covid-19 and community-acquired pneumonia based on pulmonary ct: evaluation of the diagnostic accuracy. Radiology 296(2), E65\u2013E71 (2020)","journal-title":"Radiology"},{"key":"2308_CR7","unstructured":"Farooq, M., Hafeez, A.: Covid-resnet: A deep learning framework for screening of covid19 from radiographs. arXiv preprint arXiv:2003.14395 (2020)"},{"key":"2308_CR8","doi-asserted-by":"crossref","unstructured":"Narin, A., Kaya, C., Pamuk, Z.: Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. arXiv preprint arXiv:2003.10849 (2020)","DOI":"10.1007\/s10044-021-00984-y"},{"key":"2308_CR9","unstructured":"Jiang, M., Fan, X.: Retinamask: a face mask detector. arXiv preprint arXiv:2005.03950 (2020)"},{"key":"2308_CR10","unstructured":"Wang, Z., et\u00a0al.: Masked face recognition dataset and application. arXiv preprint arXiv:2003.09093 (2020)"},{"key":"2308_CR11","unstructured":"Anwar, A., Raychowdhury, A.: Masked face recognition for secure authentication. arXiv preprint arXiv:2008.11104 (2020)"},{"key":"2308_CR12","unstructured":"Damer, N., et\u00a0al.: The effect of wearing a mask on face recognition performance: an exploratory study. In: BIOSIG (2020)"},{"key":"2308_CR13","doi-asserted-by":"publisher","first-page":"209688","DOI":"10.1109\/ACCESS.2020.3039862","volume":"8","author":"S Chen","year":"2020","unstructured":"Chen, S., Liu, W., Zhang, G.: Efficient transfer learning combined skip-connected structure for masked face poses classification. IEEE Access 8, 209688\u2013209698 (2020)","journal-title":"IEEE Access"},{"key":"2308_CR14","doi-asserted-by":"crossref","unstructured":"Boutros, F., Damer, N., et\u00a0al.: Mfr 2021: Masked face recognition competition. In: IJCB, pp. 1\u201310. IEEE (2021)","DOI":"10.1109\/IJCB52358.2021.9484337"},{"key":"2308_CR15","unstructured":"Erak$$\\iota $$n, M.E., Demir, U., Ekenel, H.K.: On recognizing occluded faces in the wild. In: BIOSIG, pp. 1\u20135. IEEE (2021)"},{"key":"2308_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.smhl.2020.100144","volume":"19","author":"A Cabani","year":"2021","unstructured":"Cabani, A., et al.: Maskedface-net-a dataset of correctly\/incorrectly masked face images in the context of covid-19. Smart Health 19, 100144 (2021)","journal-title":"Smart Health"},{"key":"2308_CR17","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: CICN, pp. 435\u2013440. IEEE (2020)","DOI":"10.1109\/CICN49253.2020.9242625"},{"key":"2308_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2020.102692","volume":"66","author":"P Nagrath","year":"2021","unstructured":"Nagrath, P., Jain, R., Madan, A., Arora, R., Kataria, P., Hemanth, J.: Ssdmnv2: a real time DNN-based face mask detection system using single shot multibox detector and mobilenetv2. Sustain. Cities Soc 66, 102692 (2021)","journal-title":"Sustain. Cities Soc"},{"issue":"5","key":"2308_CR19","doi-asserted-by":"publisher","first-page":"2070","DOI":"10.3390\/app11052070","volume":"11","author":"B Batagelj","year":"2021","unstructured":"Batagelj, B., Peer, P., \u0160truc, V., Dobri\u0161ek, S.: How to correctly detect face-masks for covid-19 from visual information? Appl. Sci. 11(5), 2070 (2021)","journal-title":"Appl. Sci."},{"key":"2308_CR20","unstructured":"Chowdary, G.J., Punn, N.S., Sonbhadra, S.K., Agarwal, S.: Face mask detection using transfer learning of inceptionv3. In: International Conference on Big Data Analytics (2020)"},{"key":"2308_CR21","unstructured":"Wang, Z., Wang, P., Louis, P.C., Wheless, L.E., Huo, Y.: Wearmask: Fast in-browser face mask detection with serverless edge computing for covid-19. arXiv preprint arXiv:2101.00784 (2021)"},{"key":"2308_CR22","unstructured":"Petrovi\u0107, N., Koci\u0107, \u0110.: Iot-based system for covid-19 indoor safety monitoring. preprint), IcETRAN (2020)"},{"key":"2308_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2020.102600","volume":"65","author":"M Loey","year":"2021","unstructured":"Loey, M., Manogaran, G., Taha, M.H.N., Khalifa, N.E.M.: Fighting against covid-19: a novel deep learning model based on yolo-v2 with resnet-50 for medical face mask detection. Sustain. Cities Soc. 65, 102600 (2021)","journal-title":"Sustain. Cities Soc."},{"key":"2308_CR24","doi-asserted-by":"publisher","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"},{"key":"2308_CR25","doi-asserted-by":"crossref","unstructured":"Sathyamoorthy, A.J., et\u00a0al.: Covid-robot: Monitoring social distancing constraints in crowded scenarios. arXiv preprint arXiv:2008.06585 (2020)","DOI":"10.1371\/journal.pone.0259713"},{"key":"2308_CR26","doi-asserted-by":"crossref","unstructured":"Yang, D., Yurtsever, E., Renganathan, V., Redmill, K.A., \u00d6zg\u00fcner, \u00dc.: A vision-based social distancing and critical density detection system for covid-19. arXiv preprint arXiv:2007.03578 pp. 24\u201325 (2020)","DOI":"10.3390\/s21134608"},{"key":"2308_CR27","doi-asserted-by":"crossref","unstructured":"Rezaei, M., Azarmi, M.: Deepsocial: social distancing monitoring and infection risk assessment in covid-19 pandemic. Appl. Sci. 10(21), 7514 (2020)","DOI":"10.3390\/app10217514"},{"key":"2308_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2020.102571","volume":"65","author":"I Ahmed","year":"2021","unstructured":"Ahmed, I., Ahmad, M., Rodrigues, J.J., Jeon, G., Din, S.: A deep learning-based social distance monitoring framework for covid-19. Sustain. Cities Soc. 65, 102571 (2021)","journal-title":"Sustain. Cities Soc."},{"key":"2308_CR29","doi-asserted-by":"crossref","unstructured":"Beyan, C., et\u00a0al.: Analysis of face-touching behavior in large scale social interaction dataset. In: ICMI (2020)","DOI":"10.1145\/3382507.3418876"},{"key":"2308_CR30","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: CVPR, pp. 4401\u20134410 (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"issue":"10","key":"2308_CR31","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 Proc. Lett. 23(10), 1499\u20131503 (2016)","journal-title":"IEEE Signal Proc. Lett."},{"key":"2308_CR32","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: CVPR, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"2308_CR33","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Ververas, E., Kotsia, I., Zafeiriou, S.: Retinaface: Single-shot multi-level face localisation in the wild. In: CVPR, pp. 5203\u20135212 (2020)","DOI":"10.1109\/CVPR42600.2020.00525"},{"key":"2308_CR34","doi-asserted-by":"crossref","unstructured":"Liu, W., et\u00a0al.: Ssd: Single shot multibox detector. In: ECCV, pp. 21\u201337. Springer (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"2308_CR35","unstructured":"Face mask detection. https:\/\/www.kaggle.com\/andrewmvd\/face-mask-detection. Accessed: 2021-05-01"},{"key":"2308_CR36","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: ICCV, pp. 3730\u20133738 (2015)","DOI":"10.1109\/ICCV.2015.425"},{"key":"2308_CR37","unstructured":"Huang, G.B., Learned-Miller, E.: Labeled faces in the wild: Updates and new reporting procedures. Dept. Comput. Sci., Univ. Massachusetts Amherst, Amherst, MA, USA, Tech. Rep 14(003) (2014)"},{"key":"2308_CR38","doi-asserted-by":"crossref","unstructured":"Yang, S., Luo, P., Loy, C.C., Tang, X.: Wider face: A face detection benchmark. In: CVPR, pp. 5525\u20135533 (2016)","DOI":"10.1109\/CVPR.2016.596"},{"key":"2308_CR39","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2308_CR40","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"2308_CR41","unstructured":"Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: ICML (2019)"},{"key":"2308_CR42","doi-asserted-by":"crossref","unstructured":"Deng, J., et\u00a0al.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"2308_CR43","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"2308_CR44","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","volume":"43","author":"J Wang","year":"2020","unstructured":"Wang, J., et al.: Deep high-resolution representation learning for visual recognition. IEEE Trans. PAMI 43, 3349\u20133364 (2020)","journal-title":"IEEE Trans. PAMI"},{"key":"2308_CR45","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: ICCV, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-022-02308-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-022-02308-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-022-02308-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T05:15:15Z","timestamp":1682313315000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-022-02308-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,22]]},"references-count":45,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["2308"],"URL":"https:\/\/doi.org\/10.1007\/s11760-022-02308-x","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,22]]},"assertion":[{"value":"1 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}