Face Counting Based on Pre-trained Machine Learning Models: A Brief Systematic Review-Reference-Cited by-同舟云学术

Face Counting Based on Pre-trained Machine Learning Models: A Brief Systematic Review

Published:2024 Issue: Volume: Page:353-364
ISSN:2367-3370
Container-title:Lecture Notes in Networks and Systems
language:en
Short-container-title:

Author:

Bengeri Anagha A.^ORCID,Jain Shashvi^ORCID,Devaranavadagi Rohit^ORCID,Pote Pallavi^ORCID,Kanamadi Sagar^ORCID,Guggari Shankru^ORCID,Kulkarni Uday^ORCID,Meena S. M.^ORCID

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-7820-5_29

Reference20 articles.

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2. Xue, B., Hu, J., Zhang, P.: Intelligent detection and recognition system for mask wearing based on improved retinaface algorithm. In: 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), pp. 474–479. IEEE (2020)

3. Deng, J., Guo, J., Ververas, E., Kotsia, I., Zafeiriou, S.: Retinaface: Single-shot multi-level face localisation in the wild. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5202–5211 (2020)

4. Chavda, A., Dsouza, J., Badgujar, S., Damani, A.: Multi-stage cnn architecture for face mask detection. In: 2021 6th International Conference for Convergence in Technology (i2ct), pp. 1–8. IEEE (2021)

5. Xiao, J., Wang, J., Cao, S., Li, Y.: Research on the improvement of mtcnn algorithm: face recognition with mask. In: 2021 2nd International Conference on Artificial Intelligence and Information Systems, pp. 1–7 (2021)