Mask Detection and Tracing System
DOI:
https://doi.org/10.32628/CSEIT201556Keywords:
Face mask, CNN, Face detection, Deep learningAbstract
Covid19 has given a new identity for wearing a mask. It is meaningful when these masked faces are detected accurately and efficiently. As a unique face detection task, face mask detection is much more difficult because of extreme occlusions which leads to the loss of face details. Besides, there is almost no existing large-scale accurately labelled masked face dataset, which increase the difficulty of face mask detection. The system encourages to use CNN-based deep learning algorithms which has done vast progress towards researches in face detection In this paper, we propose novel CNN-based method which is formed of three convolutional neural networks to detect face mask. Besides, because of the shortage of face masked training samples, we propose a new dataset called” face mask dataset” to finetune our CNN models. We evaluate our proposed face mask detection algorithm on the face mask testing set, and it achieves satisfactory performance
References
- Najma Sultana, Pintu Kumar, Monika Rani Patra, Sourabh Chandra And S.K. Safikul Alam" Sentiment Analysis For Product Review" ICTACT Journal On Soft Computing, April 2019, Volume: 09, Issue: 03
- Alpna P., Arvind K. T., “Sentiment Analysis by using Recurrent Neural Network” proceedings of 2nd ICACSE, 2019
- Yoon-Joo Park "Envisioning the Helpfulness of Online Customer Reviews across Different Product Types" MDPI, Sustainability 2018, 10, 1735
- Sunil Saumya, Jyoti Prakash Singh, Nripendra P. Rana,Yogesh k. Dwivedi, Swansea University Bay Campus, Swansea "Situating Online Consumer Reviews" Article in Electronic Commerce Research and Applications, March 2018
- Liao, Shiyang, Junbo Wang, Ruiyun Yu, Koichi Sato, and Zixue Cheng, “CNN for situations understanding based on sentiment analysis of reviews data”, Procedia Computer Science, vol. 111, pp.376-381, 2017.
- Xing Fang and Justin Zhan "Sentiment Analysis using product review data” Journal of Big Data, 2015
- D. Imamori and K. Tajima, "Foreseeing notoriety of reviews accounts through the revelation of link-propagating early adopters," in CoRR, 2015, p. 1512.
- Yeole V., P.V. Chavan, and M.C. Nikose, “Opinion mining for emotions determination”, ICIIECS 2015-2015 IEEE Int. Conf. Information, Embed. Commun. Syst., 2015.
- F. Luo, C. Li, and Z. Cao, Affective-feature-based sentiment analysis using SVM classifier, 2016 IEEE 20th Int. Conf. Comput. Support. Coop. Work Des., pp.276281, 2016.
- Kalaivani A., Thenmozhi D, Sentiment Analysis using Deep Learning Techniques., IJRTE,2018
- Bingwei Liu, Erik Blasch, Yu Chen, Dan Shen, and Genshe Chen. Scalable sentiment classification for big data analysis using naïve bayes classifier. In big data, 2013 IEEE International Conference on, pages 99-104. IEEE,2013
- D. N. Devi, C. K. Kumar, and S. Prasad, “A feature based approach for sentiment analysis by using support vector machine,” in Advanced Computing (IACC), 2016 IEEE 6thInterna-tional Conference on. IEEE, 2016, pp. 3–8.
- McAuley, J.; Leskovec, J. Hidden factors and hidden topics: Understanding rating dimensions with review text. In Proceedings of the 7th ACM Conference on Recommender Systems, RecSys’, Hong Kong, China,12–16 October 2013; pp. 165–172.
- Ghose, A.; Ipeirotis, P.G. Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Trans. Knowl. Data Eng. 2011, 23, 1498–1512.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRCSEIT

This work is licensed under a Creative Commons Attribution 4.0 International License.