The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Automatic Age Estimation Based on Facial Aging Patterns
Abstract
While recognition of most facial variations, such as identity, expression and gender, has been extensively studied, automatic age estimation has rarely been explored. In contrast to other facial variations, aging variation presents several unique characteristics which make age estimation a challenging task. This paper proposes an automatic age estimation method named AGES (AGing pattErn Subspace). The basic idea is to model the aging pattern, which is defined as the sequence of a particular individual' s face images sorted in time order, by constructing a representative subspace. The proper aging pattern for a previously unseen face image is determined by the projection in the subspace that can reconstruct the face image with minimum reconstruction error, while the position of the face image in that aging pattern will then indicate its age. In the experiments, AGES and its variants are compared with the limited existing age estimation methods (WAS and AAS) and some well-established classification methods (kNN, BP, C4.5, and SVM). Moreover, a comparison with human perception ability on age is conducted. It is interesting to note that the performance of AGES is not only significantly better than that of all the other algorithms, but also comparable to that of the human observers.
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Cited by
Age Synthesis and Estimation via Faces: A Survey
Fu, Y., Guo, G., & Huang, T. S. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.36 · Cited by 569
Three-dimensional human facial morphologies as robust aging markers
Chen, W., Qian, W., Wu, G., Chen, W., Xian, B., Chen, X., Cao, Y., Green, C. D., Zhao, F., Tang, K., et al. (2015) · Cell Research · doi:10.1038/cr.2015.36 · 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
Personalized 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
Deep Differentiable Random Forests for Age Estimation
Shen, W., Guo, Y., Wang, Y., Zhao, K., Wang, B., & Yuille, A. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2937294 · Cited by 91
Metadata
- Authors
- Geng, X., Zhou, Z.-H., & Smith-Miles, K.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2007-12
- Volume
- 29
- Issue
- 12
- Pages
- 2234-2240
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2106488920
- Cited by
- 964
- References
- 29
- FWCI
- 36.66
- Type
- journal article
- Language
- en
- Open access
- closed
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- Geng, X., Zhou, Z.-H., & Smith-Miles, K. (2007). Automatic Age Estimation Based on Facial Aging Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2007.70733
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