The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
AgeDB: The First Manually Collected, In-the-Wild Age Database
Abstract
Over the last few years, increased interest has arisen with respect to age-related tasks in the Computer Vision community. As a result, several "in-the-wild" databases annotated with respect to the age attribute became available in the literature. Nevertheless, one major drawback of these databases is that they are semi-automatically collected and annotated and thus they contain noisy labels. Therefore, the algorithms that are evaluated in such databases are prone to noisy estimates. In order to overcome such drawbacks, we present in this paper the first, to the best of knowledge, manually collected "in-the-wild" age database, dubbed AgeDB, containing images annotated with accurate to the year, noise-free labels. As demonstrated by a series of experiments utilizing state-of-the-art algorithms, this unique property renders AgeDB suitable when performing experiments on age-invariant face verification, age estimation and face age progression "in-the-wild".
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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
Self-citationRetinaFace: Single-Shot Multi-Level Face Localisation in the Wild
Deng, J., Guo, J., Ververas, E., Kotsia, I., & Zafeiriou, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00525 · Cited by 1,681
Self-citationMobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
Chen, S., Liu, Y., Gao, X., & Han, Z. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-97909-0_46 · Cited by 771
AdaFace: Quality Adaptive Margin for Face Recognition
Kim, M., Jain, A. K., & Liu, X. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01819 · Cited by 584
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
- Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., & Zafeiriou, S.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 1997-2005
- OpenAlex
- W2663800299
- Cited by
- 800
- References
- 49
- FWCI
- 10.68
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., & Zafeiriou, S. (2017). AgeDB: The First Manually Collected, In-the-Wild Age Database. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/cvprw.2017.250
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