The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Age-Invariant Face Recognition
Abstract
One of the challenges in automatic face recognition is to achieve temporal invariance. In other words, the goal is to come up with a representation and matching scheme that is robust to changes due to facial aging. Facial aging is a complex process that affects both the 3D shape of the face and its texture (e.g., wrinkles). These shape and texture changes degrade the performance of automatic face recognition systems. However, facial aging has not received substantial attention compared to other facial variations due to pose, lighting, and expression. We propose a 3D aging modeling technique and show how it can be used to compensate for the age variations to improve the face recognition performance. The aging modeling technique adapts view-invariant 3D face models to the given 2D face aging database. The proposed approach is evaluated on three different databases (i.g., FG-NET, MORPH, and BROWNS) using FaceVACS, a state-of-the-art commercial face recognition engine.
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Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.927467 · Cited by 5,409
Thompson, D. W. (1992) · doi:10.1017/cbo9781107325852 · Cited by 443
Cited by
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Chen, B.-C., Chen, C.-S., & Hsu, W. H. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2420374 · Cited by 305
Futures of artificial intelligence through technology readiness levels
Martínez-Plumed, F., Gómez, E., & Hernández-Orallo, J. (2021) · Telematics and Informatics · doi:10.1016/j.tele.2020.101525 · Cited by 130
Decorrelated Adversarial Learning for Age-Invariant Face Recognition
Wang, H., Gong, D., Li, Z., & Liu, W. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00364 · Cited by 115
Face matching and retrieval in forensics applications
Jain, A. K., Klare, B., & Park, U. (2012) · IEEE Multimedia · doi:10.1109/mmul.2012.4 · Cited by 105
Self-citationPersonalized Age Progression with Bi-Level Aging Dictionary Learning
Shu, X., Tang, J., Li, Z., Lai, H., Zhang, L., & Yan, S. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2705122 · Cited by 95
Metadata
- Authors
- Park, U., Tong, Y., & Jain, A. K.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2010-05
- Volume
- 32
- Issue
- 5
- Pages
- 947-954
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2111084364
- Cited by
- 415
- References
- 2
- FWCI
- 25.43
- Type
- journal article
- Language
- en
- Open access
- closed
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- Park, U., Tong, Y., & Jain, A. K. (2010). Age-Invariant Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2010.14
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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.
