The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Masked Face Recognition Dataset and Application
Open accessAbstract
During COVID-19 coronavirus epidemic, almost everyone wears a mask to prevent the spread of virus. It raises a problem that the traditional face recognition model basically fails in the scene of face-based identity verification, such as security check, community visit check-in, etc. Therefore, it is imminent to boost the performance of masked face recognition. Most recent advanced face recognition methods are based on deep learning, which heavily depends on a large number of training samples. However, there are presently no publicly available masked face recognition datasets, especially real ones. To this end, this work proposes three types of masked face datasets, including Masked Face Detection Dataset (MFDD), Real-world Masked Face Recognition Dataset (RMFRD) and Synthetic Masked Face Recognition Dataset (SMFRD). Besides, we conduct benchmark experiments on these three datasets for reference. As far as we know, we are the first to publicly release large-scale masked face recognition datasets that can be downloaded for free athttps://github.com/X-zhangyang/Real-World-Masked-Face-Dataset.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00482 · Cited by 7,781
CosFace: Large Margin Cosine Loss for Deep Face Recognition
Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., & Liu, W. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00552 · Cited by 3,034
SphereFace: Deep Hypersphere Embedding for Face Recognition
Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., & Song, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.713 · Cited by 2,280
WIDER FACE: A Face Detection Benchmark
Yang, S., Luo, P., Loy, C. C., & Tang, X. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.596 · Cited by 1,830
Learning Face Representation from Scratch
Yi, D., Lei, Z., Liao, S., & Li, S. Z. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.7923 · Cited by 1,521
Bulat, A., & Tzimiropoulos, G. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.116 · Cited by 1,464
Bulat, A., & Tzimiropoulos, G. (2017) · Nottingham ePrints (University of Nottingham) · Cited by 1,277
Frontal to profile face verification in the wild
Sengupta, S., Chen, J.-C., Castillo, C., Patel, V. M., Chellappa, R., & Jacobs, D. W. (2016) · 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2016.7477558 · Cited by 805
AgeDB: The First Manually Collected, In-the-Wild Age Database
Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., & Zafeiriou, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.250 · Cited by 800
Detecting Masked Faces in the Wild with LLE-CNNs
Ge, S., Li, J., Ye, Q., & Luo, Z. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.53 · Cited by 454
Cited by
Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey
Nguyen, D. C., Ding, M., Pathirana, P. N., & Seneviratne, A. (2021) · IEEE Access · doi:10.1109/access.2021.3093633 · Cited by 286
Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4
Yu, J., & Zhang, W. (2021) · Sensors · doi:10.3390/s21093263 · Cited by 280
Ngan, M. L., Grother, P. J., & Hanaoka, K. (2020) · doi:10.6028/nist.ir.8331 · Cited by 62
Zhu, J., Guo, Q., Juefei-Xu, F., Huang, Y., Liu, Y., & Pu, G. (2022) · arXiv · doi:10.48550/arxiv.2201.06427 · Cited by 3
Metadata
- Authors
- Wang, Z., Huang, B., Wang, G., Yi, P., & Jiang, K.
- Venue
- IEEE Transactions on Biometrics, Behavior, and Identity Science
- Published
- 2023-04
- Volume
- 5
- Issue
- 2
- Pages
- 298-304
- ISSN
- 2637-6407 (ISSN-L)
- OpenAlex
- W4287825176
- Cited by
- 473
- References
- 22
- FWCI
- 20.28
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Masked Face Recognition Dataset and Application. https://app.cochat.ai/papers/10.1109/tbiom.2023.3242085
- The paper
- Wang, Z., Huang, B., Wang, G., Yi, P., & Jiang, K. (2023). Masked Face Recognition Dataset and Application. IEEE Transactions on Biometrics, Behavior, and Identity Science. https://doi.org/10.1109/tbiom.2023.3242085
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
