Face mask detection in foggy weather from digital images using transfer learning

References

• Abdulrahman AA, Rasheed MS, Shihab SN. The analytic of image processing smoothing spaces using wavelet. J Phys Conf Ser. 2021;1897:1–15. doi:10.1088/1742-6596/1879/2/022118.
   Google Scholar
• laith S, Abdulrahman AA, Al-Azawi FST. Face detection for color image based on MATLAB, Published in Journal of Physics Conference Series, vol 1879, no. 022129, 1–10, 2021. doi:10.1088/1742-6596/1879/2/022129.
   Google Scholar
• Abdulrahman AA, Tahir FS. Face recognition using enhancement discrete wavelet transform based on MATLAB. Indonesian J Elect Eng Comput Sci. 2021;23:1128–1136. doi:10.11591/ijeecs.v23.i2.pp1128-1136.
   Google Scholar
• Abduldaim AM, Abdulrahman AA, Tahir FS. The effectiveness of discrete hermite wavelet filters technique in digital image watermarking. Indonesian J Elect Eng Comput Sci. 2021;23:1128–1136. doi:10.11591/ijeecs.v23.i2.pp1128-1136.
   Google Scholar
• Mohammed SA, Abdulrahman AA, Tahir FS. Emotions students’ faces recognition using hybrid deep learning and discrete chebyshev wavelet transformations. Intern J Math Comput Sci. 2022;17(3):1405–1417. http://ijmcs.future-in-tech.net.
   Web of Science ®Google Scholar
• He K, Sun J, Tang X. Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell. 2010;33(12):2341–2353. doi:10.1109/TPAMI.2010.168.
   PubMed Web of Science ®Google Scholar
• Koranga P, Singar S, Gupta S. Classification of homogeneous and Non homogeneous single image dehazing techniques. In: International conference on emerging technologies in computer engineering. Cham.: Springer; 2022. p. 479–493.
   Google Scholar
• Loey M, Manogaran G, Taha MHN, et al. A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic. Measurement (Mahwah N J). 2021;167:108288), doi:10.1016/j.measurement.2020.108288.
   Google Scholar
• Loey M, Manogaran G, Taha MHN, et al. Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection. Sustain Cities Soc. 2021;65:102600), doi:10.1016/j.scs.2020.102600.
   PubMed Web of Science ®Google Scholar
• Ting DSW, Carin L, Dzau V, et al. Digital technology and COVID-19. Nat Med. 2020;26(4):459–461. doi:10.1038/s41591-020-0824-5.
   PubMed Web of Science ®Google Scholar
• Zhang N, Zhang L, Cheng Z. Towards simulating foggy and hazy images and evaluating their authenticity. In: International conference on neural information processing. Cham: Springer; 2017. p. 405–415.
   Google Scholar
• Qin B, Li D. Identifying facemask-wearing condition using image super-resolution with classification network to prevent COVID-19. Sensors. 2020;20(18):5236), doi:10.3390/s20185236.
   PubMed Web of Science ®Google Scholar
• Inamdar M, Mehendale N. Real-time face mask identification using facemasknet deep learning network. Available at SSRN, 3663305; 2020.
   Google Scholar
• Howard AG, Zhu M, Chen B, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861; 2017.
   Google Scholar
• Sandler M, Howard A, Zhu M, et al. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition: 4510–4520; 2018.
   Google Scholar
• He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition: 770–778; 2016.
   Google Scholar
• Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556; 2014.
   Google Scholar
• Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition: 1–9; 2015.
   Google Scholar
• Nieto-Rodriguez A, Mucientes M, Brea VM. System for medical mask detection in the operating room through facial attributes. In: Iberian conference on pattern recognition and image analysis. Cham: Springer; 2015. p. 138–145.
   Google Scholar
• Yadav S. Deep learning based safe social distancing and face mask detection in public areas for covid-19 safety guidelines adherence. Intern J Res Appl Sci Eng Technol. 2020;8(7):1368–1375. doi:10.22214/ijraset.2020.30560.
   Google Scholar
• Jiang M, Fan X, Yan H. Retinamask: A face mask detector. arXiv preprint arXiv:2005.03950; 2020.
   Google Scholar
• Lin T-Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition: 2117–2125; 2017.
   Google Scholar
• Zou Z, Shi Z, Guo Y, et al. Object detection in 20 years: A survey. arXiv preprint arXiv:1905.05055; 2019.
   Google Scholar
• Ristea N-C, Ionescu RT. Are you wearing a mask? Improving mask detection from speech using augmentation by cycle-consistent GANs. arXiv preprint arXiv:2006.10147; 2020.
   Google Scholar
• Mercaldo F, Santone A. Transfer learning for mobile real-time face mask detection and localization. J Amer Med Inform Assoc. 2021;28(7):1548–1554. doi:10.1093/jamia/ocab052.
   PubMed Web of Science ®Google Scholar
• Jignesh C, Narinder Singh Punn G, Kumar Sonbhadra S, et al. Face mask detection using transfer learning of inceptionv3. In: International conference on Big data analytics. Cham: Springer; 2020. p. 81–90.
   Google Scholar
• Addagarla SK, Chakravarthi GK, Anitha P. Real time multi-scale facial mask detection and classification using deep transfer learning techniques. Intern J. 2020;9(4):4402–4408.
   Google Scholar
• Zhou JT, Jialin Pan S, Tsang IW. A deep learning framework for hybrid heterogeneous transfer learning. Artif Intell. 2019;275:310–328. doi:10.1016/j.artint.2019.06.001.
   Web of Science ®Google Scholar
• Zhao W. Research on the deep learning of the small sample data based on transfer learning. In: AIP conference proceedings, vol. 1864, no. 1. GT: AIP Publishing LLC; 2017. p. 020018.
   Google Scholar
• Ng H-W, Nguyen VD, Vonikakis V, et al. Deep learning for emotion recognition on small datasets using transfer learning. In Proceedings of the 2015 ACM on international conference on multimodal interaction: 443–449; 2015.
   Google Scholar
• Khan S, Islam N, Jan Z, et al. A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognit Lett. 2019;125:1–6. doi:10.1016/j.patrec.2019.03.022.
   Web of Science ®Google Scholar
• Kansal I, Popli R, Verma J, et al. Digital image processing and IoT in smart health care -A review. 2022 International Conference on Emerging Smart Computing and Informatics (ESCI): 1–6; 2022. doi:10.1109/ESCI53509.2022.9758227.
   Google Scholar
• Lu L, Zhou Y, Paneta K, et al. Comparative study of histogram equalization algorithms for image enhancement. Mob Multim Image Process Security Applic. 2010;7708(2010):337–347.
   Google Scholar
• Reza AM. Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. J VLSI Signal Process Syst Signal Image Video Technol. 2004;38(1):35–44. doi:10.1023/B:VLSI.0000028532.53893.82.
   Google Scholar
• Berman D, Avidan S. Non-local image dehazing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition: 1674–1682; 2016.
   Google Scholar
• Cai B, Xu X, Jia K, et al. Dehazenet: An end-to-end system for single image haze removal. IEEE Trans Image Process. 2016;25(11):5187–5198. doi:10.1109/TIP.2016.2598681.
   PubMed Web of Science ®Google Scholar
• Ren W, Liu S, Zhang H, et al. Single image dehazing via multi-scale convolutional neural networks. In: European conference on computer vision. Cham: Springer; 2016. p. 154–169.
   Google Scholar
• Zhang H, Patel VM. Densely connected pyramid dehazing network. In Proceedings of the IEEE conference on computer vision and pattern recognition: 3194–3203; 2018.
   Google Scholar
• Ren W, Zhang J, Xu X, et al. Deep video dehazing with semantic segmentation. IEEE Trans Image Process. 2018;28(4):1895–1908. doi:10.1109/TIP.2018.2876178.
   Web of Science ®Google Scholar
• Li R, Pan J, Li Z, et al. Single image dehazing via conditional generative adversarial network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition: 8202–8211; 2018.
   Google Scholar
• Ren W, Ma L, Zhang J, et al. Gated fusion network for single image dehazing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition: 3253–3261; 2018.
   Google Scholar
• Liu X, Ma Y, Shi Z, et al. Griddehazenet: Attention-based multi-scale network for image dehazing. In Proceedings of the IEEE/CVF International Conference on Computer Vision: 7314–7323; 2019.
   Google Scholar
• Dong H, Pan J, Xiang L, et al. Multi-scale boosted dehazing network with dense feature fusion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition: 2157–2167; 2020.
   Google Scholar
• Wu H, Qu Y, Lin S, et al. Contrastive learning for compact single image dehazing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition: 10551–10560; 2021.
   Google Scholar
• Kansal I, Singh Kasana S. Fusion-based image de-fogging using dual tree complex wavelet transform. Int J Wavelets Multiresolut Inf Process. 2018;16(06):1850054), doi:10.1142/S0219691318500546.
   Google Scholar
• Kansal I, Singh Kasana S. Minimum preserving subsampling-based fast image de-fogging. J Mod Opt. 2018;65(18):2103–2123. doi:10.1080/09500340.2018.1499976.
   Web of Science ®Google Scholar
• Kansal I, Singh Kasana S. Improved color attenuation prior based image de-fogging technique. Multimedia Tos Applic. 2020;79(17):12069–12091. doi:10.1007/s11042-019-08240-6.
   Web of Science ®Google Scholar