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IARPA Janus Benchmark - C: Face Dataset and Protocol
Abstract
Although considerable work has been done in recent years to drive the state of the art in facial recognition towards operation on fully unconstrained imagery, research has always been restricted by a lack of datasets in the public domain. In addition, traditional biometrics experiments such as single image verification and closed set recognition do not adequately evaluate the ways in which unconstrained face recognition systems are used in practice. The IARPA Janus Benchmark-C (IJB-C) face dataset advances the goal of robust unconstrained face recognition, improving upon the previous public domain IJB-B dataset, by increasing dataset size and variability, and by introducing end-to-end protocols that more closely model operational face recognition use cases. IJB-C adds 1,661 new subjects to the 1,870 subjects released in IJB-B, with increased emphasis on occlusion and diversity of subject occupation and geographic origin with the goal of improving representation of the global population. Annotations on IJB-C imagery have been expanded to allow for further covariate analysis, including a spatial occlusion grid to standardize analysis of occlusion. Due to these enhancements, the IJB-C dataset is significantly more challenging than other datasets in the public domain and will advance the state of the art in unconstrained face recognition.
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Citations
Cites
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267
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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
Domain Generalization: A Survey
Zhou, K., Liu, Z., Qiao, Y., Xiang, T., & Loy, C. C. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3195549 · Cited by 1,315
Circle Loss: A Unified Perspective of Pair Similarity Optimization
Sun, Y., Cheng, C., Zhang, Y., Zhang, C., Zheng, L., Wang, Z., & Wei, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00643 · Cited by 987
FSGAN: Subject Agnostic Face Swapping and Reenactment
Nirkin, Y., Keller, Y., & Hassner, T. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00728 · Cited by 706
CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition
Huang, Y., Wang, Y., Tai, Y., Liu, X., Shen, P., Li, S., Li, J., & Huang, F. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00594 · Cited by 580
Metadata
- Authors
- Maze, B., Adams, J., Duncan, J. A., Kalka, N., Miller, T., Otto, C., Jain, A. K., Niggel, W. T., Anderson, J., Cheney, J., & Grother, P.
- Venue
- 2018 International Conference on Biometrics (ICB)
- Publisher
- IEEE
- Published
- 2018-02
- Pages
- 158-165
- OpenAlex
- W2871667416
- Cited by
- 639
- References
- 1
- FWCI
- 19.54
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Maze, B., Adams, J., Duncan, J. A., Kalka, N., Miller, T., Otto, C., Jain, A. K., Niggel, W. T., Anderson, J., Cheney, J., & Grother, P. (2018). IARPA Janus Benchmark - C: Face Dataset and Protocol. 2018 International Conference on Biometrics (ICB). https://doi.org/10.1109/icb2018.2018.00033
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