Real Time Face Recognition Software Development for People With and Without Face Masks
DOI:
https://doi.org/10.5564/jimdt.v4i1.2662Keywords:
Casia-Webface, Facenet, Deep learningAbstract
In recent years, people began to wear masks to prevent the spread of Covid-19, which is widespread all over the world. Face recognition algorithms tend to be relatively inadequate for people wearing masks because masks cover most of a person’s face. Our goal is to develop software with high accuracy and precision on the task which is recognizing people with and without face masks. In this paper, we are introducing some results of our work.
Амны Хаалттай болон Амны Хаалтгүй Хүний Царайг Бодит Хугацаанд Таних Программын Хөгжүүлэлт
Хураангуй: Ковид 19 цар тахлын эрсдэлээс сэргийлэхийн тулд хүмүүс амны хаалт хэрэглэх болсон. Амны хаалт нь хүний нүүрний ихэнх хэсгийг далдалдгаас үүдэн царай таних алго ритмууд амны хаалттай хүнийг муу таних хандлагатай байдаг. Үүнийг дагаад амны хаалттай хүнийг хэн бэ? гэдгийг таних царай танилтын алгоритмыг боловсруулах хэрэгцээ үүссэн. Бид аль болох үр дүнтэй, өндөр нарийвчлалтай, бодит хугацаанд ажиллаж болохуйц амны хаалттай болон амны хаалтгүй хүний таних алгоритмыг гарган авах, түүн дээр тулгуурлан программ хөгжүүлэх зорилготойгоор ажилласан бөгөөд энэхүү өгүүллээр бид бодит хугацаанд амны хаалттай болон амны хаалтгүй хүний царай таних алгоритм ба программын хөгжүүлэлтийн өнөөдрийг хүртэлх зарим үр дүнг танилцуулна.
Түлхүүр үгс: Casia-Webface, Facenet, Гүн сургалт
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References
C. Liu, and H. Wechsler, “Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition,” Image processing, IEEE Transactions on, vol. 11, no. 4, pp. 467–476, 2002, doi: https://doi.org/10.1109/TIP.2002.999679.
M. Turk, and A. Pentland, “Eigenfaces for recognition,” Journal of cognitive neuroscience, Vol 3, no. 1, pp. 71–86, 1991, doi: https://doi.org/10.1515/ijsl.1991.87.71.
G. B. Huang, M. Ramesh, and T. Berg, “Labeled faces in the wild: A database for studying face recognition in unconstrained environments,” Technical Report, University of Massachusetts, Amherst, pp. 07-49, 2007.
D. Chen, X. Cao, and F. Wen, “Blessing of dimensionality: High-dimensional feature and its efficient compression for face verification,” Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3025–3032, 2013, doi: https://doi.org/10.1109/CVPR.2013.389.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems, pp. 1097–1105, 2012.
Y. Taigman, M. Yang, and M. Ranzato, “Deepface: Closing the gap to human-level performance in face verification,” Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1701–1708, 2014, doi: https://doi.org/10.1109/CVPR.2014.220.
Y. Sun, Y. Chen, and X. Wang, “Deep learning face representation by joint identification verification,” Advances in neural information processing systems, pp. 1988–1996, 2014.
F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 815–823, 2015, doi: https://doi.org/10.1109/CVPR.2015.7298682.
J. Liu, Y. Deng, T. Bai, and Z. Wei, “Targeting ultimate accuracy: Face recognition via deep embedding,” arXiv preprint arXiv:1506.07310, 2015.
O. M. Parkhi, A. Vedaldi, A. Zisserman, “Deep face recognition,” BMVC, Vol. 1, no. 3, pp. 6, 2015, doi: https://doi.org/10.5244/C.29.41.
W. Liu, Y. Wen, and Z. Yu, “Sphereface: Deep hypersphere embedding for face recognition,” Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 212–220, 2017, doi: https://doi.org/10.1109/CVPR.2017.713.
J. Deng, J. Guo, and N. Xue, “Arcface: Additive angular margin loss for deep face recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4690–4699, 2019, doi: https://doi.org/10.1109/CVPR.2019.00482.
H. Wang, Y. Wang, and Z. Zhou, “Cosface: Large margin cosine loss for deep face recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5265–5274, 2018, doi: https://doi.org/10.1109/CVPR.2018.00552.
Y. B. Chandra, and G. K. Reddy, “A Comparative Analysis Of Face Recognition Models On Masked Faces,” INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH, Vol. 9, no. 10, 2020, doi: https://doi.org/10.1016/S0958-2118(20)30175-0.
D. Yi, Z. Lei, Sh. Liao, and S. Z. Li, “Learning Face Representation from Scratch,” CoRR, 2014.
Н. Даваасүрэн, Ө. Баттулга, Б. Луубаатар, Д. Ууганбаатар, “Амны хаалттай хүнийг таних гүн сургалтын загвар”, Хүрэлтогоот-2021 эрдэм шинжилгээний хурлын эмхэтгэл, pp. 48-55, 2021, doi: https://doi.org/10.1055/a-1347-3272.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.Y. Fu, and A. C. Berg, “SSD: Single shot multibox detector”, ХECCV, 2016, doi: https://doi.org/10.1007/978-3-319-46448-0_2.
A. Anwar, and A. Raychowdhury, “Masked Face Recognition for Secure Authentication”, arXiv,2020.
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