{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T04:36:18Z","timestamp":1780547778048,"version":"3.54.1"},"reference-count":36,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,24]],"date-time":"2021-09-24T00:00:00Z","timestamp":1632441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As the interest in facial detection grows, especially during a pandemic, solutions are sought that will be effective and bring more benefits. This is the case with the use of thermal imaging, which is resistant to environmental factors and makes it possible, for example, to determine the temperature based on the detected face, which brings new perspectives and opportunities to use such an approach for health control purposes. The goal of this work is to analyze the effectiveness of deep-learning-based face detection algorithms applied to thermal images, especially for faces covered by virus protective face masks. As part of this work, a set of thermal images was prepared containing over 7900 images of faces with and without masks. Selected raw data preprocessing methods were also investigated to analyze their influence on the face detection results. It was shown that the use of transfer learning based on features learned from visible light images results in mAP greater than 82% for half of the investigated models. The best model turned out to be the one based on Yolov3 model (mean average precision\u2014mAP, was at least 99.3%, while the precision was at least 66.1%). Inference time of the models selected for evaluation on a small and cheap platform allows them to be used for many applications, especially in apps that promote public health.<\/jats:p>","DOI":"10.3390\/s21196387","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T22:16:38Z","timestamp":1632780998000},"page":"6387","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Face with Mask Detection in Thermal Images Using Deep Neural Networks"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5839-9013","authenticated-orcid":false,"given":"Natalia","family":"G\u0142owacka","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11\/12, 80-233 Gdansk, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2266-0088","authenticated-orcid":false,"given":"Jacek","family":"Rumi\u0144ski","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11\/12, 80-233 Gdansk, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kwa\u015bniewska, A., Rumi\u0144ski, J., and Rad, P. (2017, January 17\u201319). Deep features class activation map for thermal face detection and tracking. Proceedings of the 2017 10th International Conference on Human System Interactions (HSI), Ulsan, Korea.","DOI":"10.1109\/HSI.2017.8004993"},{"key":"ref_2","unstructured":"Wu, Z., Peng, M., and Chen, T. (2016, January 10\u201312). Thermal face recognition using convolutional neural network. Proceedings of the 2016 International Conference on Optoelectronics and Image Processing (ICOIP), Warsaw, Poland."},{"key":"ref_3","first-page":"159","article-title":"Model-based parametric images in dynamic thermography","volume":"6","author":"Ruminski","year":"2000","journal-title":"Pol. J. Med. Phys. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-41172-7","article-title":"Detecting changes in facial temperature induced by a sudden auditory stimulus based on deep learning-assisted face tracking","volume":"9","author":"Sonkusare","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ruminski, J., and Kwasniewska, A. (2017). Evaluation of respiration rate using thermal imaging in mobile conditions. Application of Infrared to Biomedical Sciences, Springer Nature.","DOI":"10.1007\/978-981-10-3147-2_18"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1016\/j.bbe.2016.07.006","article-title":"Analysis of the parameters of respiration patterns extracted from thermal image sequences","volume":"36","year":"2016","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103263","DOI":"10.1016\/j.engappai.2019.103263","article-title":"Super-resolved thermal imagery for high-accuracy facial areas detection and analysis","volume":"87","author":"Kwasniewska","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kwasniewska, A., Szankin, M., Ruminski, J., Sarah, A., and Gamba, D. (2021, January 19-25). Improving Accuracy of Respiratory Rate Estimation by Restoring High Resolution Features with Transformers and Recursive Convolutional Models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00427"},{"key":"ref_9","unstructured":"Reese, K., Zheng, Y., and Elmaghraby, A. (2012, January 7\u20138). A comparison of face detection algorithms in visible and thermal spectrums. Proceedings of the Int\u2019l Conference on Advances in Computer Science and Application, Amsterdam, The Netherlands."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Friedrich, G., and Yeshurun, Y. (2002). Seeing people in the dark: Face recognition in infrared images. International Workshop on Biologically Motivated Computer Vision, Springer.","DOI":"10.1007\/3-540-36181-2_35"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","article-title":"Robust real-time face detection","volume":"57","author":"Viola","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., and Li, S.Z. (2017, January 1\u20134). FaceBoxes: A CPU real-time face detector with high accuracy. Proceedings of the 2017 IEEE International Joint Conference on Biometrics (IJCB), Denver, CO, USA.","DOI":"10.1109\/BTAS.2017.8272675"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yang, W., and Jiachun, Z. (2018, January 23\u201327). Real-time face detection based on YOLO. Proceedings of the 2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII), Jeju, Korea.","DOI":"10.1109\/ICKII.2018.8569109"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yang, S., Luo, P., Loy, C.C., and Tang, X. (2016, January 27\u201330). Wider face: A face detection benchmark. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.596"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Pang, L., Ming, Y., and Chao, L. (2018, January 12\u201316). F-DR Net: Face detection and recognition in One Net. Proceedings of the 2018 14th IEEE International Conference on Signal Processing (ICSP), Beijing, China.","DOI":"10.1109\/ICSP.2018.8652436"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hussain, S., Yu, Y., Ayoub, M., Khan, A., Rehman, R., Wahid, J.A., and Hou, W. (2021). IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19. Appl. Sci., 11.","DOI":"10.3390\/app11083495"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kopaczka, M., Nestler, J., and Merhof, D. (2017). Face Detection in Thermal Infrared Images: A Comparison of Algorithm- and Machine-Learning-Based Approaches. Advanced Concepts for Intelligent Vision Systems, Springer.","DOI":"10.1007\/978-3-319-70353-4_44"},{"key":"ref_18","first-page":"886","article-title":"Histograms of Oriented Gradients for Human Detection","volume":"1","author":"Dalal","year":"2005","journal-title":"Comput. Vis. Pattern Recognit."},{"key":"ref_19","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Silva, G., Monteiro, R., Ferreira, A., Carvalho, P., and Corte-Real, L. (2019). Face Detection in Thermal Images with YOLOv3. Innternational Symposium on Visual Computing, Springer.","DOI":"10.1007\/978-3-030-33723-0_8"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Peng, M., Wang, C., Chen, T., and Liu, G. (2016). NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification. Information, 7.","DOI":"10.3390\/info7040061"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_24","first-page":"1097","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"Volume 25","author":"Pereira","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1109\/TPAMI.2007.1014","article-title":"Illumination Invariant Face Recognition Using Near-Infrared Images","volume":"29","author":"Li","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.3844\/jcssp.2018.1627.1637","article-title":"Thermal Face Authentication with Convolutional Neural Network","volume":"14","author":"Sayed","year":"2018","journal-title":"J. Comput. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Nikisins, O., Nasrollahi, K., Greitans, M., and Moeslund, T.B. (2014, January 24\u201328). RGB-D-T Based Face Recognition. Proceedings of the 2014 22nd International Conference on Pattern Recognition, Stockholm, Sweden.","DOI":"10.1109\/ICPR.2014.302"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Su, J.W., Chu, H.K., and Huang, J.B. (2020, January 13\u201319). Instance-aware image colorization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00799"},{"key":"ref_29","unstructured":"(2021, January 05). Ultra-Lightweight Face Detection Model. Available online: https:\/\/github.com\/Linzaer\/Ultra-Light-Fast-Generic-Face-Detector-1MB."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Yuxiang, Z., Yu, J., Kotsia, I., and Zafeiriou, S. (2019). RetinaFace: Single-stage Dense Face Localisation in the Wild. arXiv.","DOI":"10.1109\/CVPR42600.2020.00525"},{"key":"ref_31","unstructured":"(2021, January 10). RetinaFace in PyTorch. Available online: https:\/\/github.com\/biubug6\/Pytorch_Retinaface."},{"key":"ref_32","unstructured":"He, Y., Xu, D., Wu, L., Jian, M., Xiang, S., and Pan, C. (2019). LFFD: A Light and Fast Face Detector for Edge Devices. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sch\u00fctze, H., Manning, C.D., and Raghavan, P. (2008). Introduction to Information Retrieval, Cambridge University Press.","DOI":"10.1017\/CBO9780511809071"},{"key":"ref_34","unstructured":"Powers, D.M. (2020). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, D., Zhu, X., Chen, X., Tian, D., Hu, X., and Qin, G. (2021, January 29\u201330). Thermal Imaging Face Detection Based on Transfer Learning. Proceedings of the 2021 6th International Conference on Smart Grid and Electrical Automation (ICSGEA), Kunming, China.","DOI":"10.1109\/ICSGEA53208.2021.00064"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Mallat, K., and Dugelay, J.L. (2018, January 26\u201328). A benchmark database of visible and thermal paired face images across multiple variations. Proceedings of the 2018 International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany.","DOI":"10.23919\/BIOSIG.2018.8553431"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/19\/6387\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:04:28Z","timestamp":1760166268000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/19\/6387"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,24]]},"references-count":36,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["s21196387"],"URL":"https:\/\/doi.org\/10.3390\/s21196387","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,24]]}}}