The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Ordinal hyperplanes ranker with cost sensitivities for age estimation
Abstract
In this paper, we propose an ordinal hyperplane ranking algorithm called OHRank, which estimates human ages via facial images. The design of the algorithm is based on the relative order information among the age labels in a database. Each ordinal hyperplane separates all the facial images into two groups according to the relative order, and a cost-sensitive property is exploited to find better hyperplanes based on the classification costs. Human ages are inferred by aggregating a set of preferences from the ordinal hyperplanes with their cost sensitivities. Our experimental results demonstrate that the proposed approach outperforms conventional multiclass-based and regression-based approaches as well as recently developed ranking-based age estimation approaches.
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Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Libsvm : A library for support vector machines
Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,122
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Learning to rank using gradient descent
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Large Margin Rank Boundaries for Ordinal Regression
Herbrich, R., Graepel, T., & Obermayer, K. (2000) · Advances in Large-Margin Classifiers · doi:10.7551/mitpress/1113.003.0010 · Cited by 1,074
Automatic Age Estimation Based on Facial Aging Patterns
Geng, X., Zhou, Z.-H., & Smith-Miles, K. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70733 · Cited by 964
MORPH: A Longitudinal Image Database of Normal Adult Age-Progression
Ricanek, K., & Tesafaye, T. (2006) · 7th International Conference on Automatic Face and Gesture Recognition (FGR06) · doi:10.1109/fgr.2006.78 · Cited by 778
Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression
Guo, G., Fu, Y., Dyer, C. R., & Huang, T. S. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2008.924280 · Cited by 645
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Human age estimation using bio-inspired features
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Cited by
Face Recognition and Retrieval Using Cross-Age Reference Coding With Cross-Age Celebrity Dataset
Chen, B.-C., Chen, C.-S., & Hsu, W. H. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2420374 · Cited by 305
Self-citationA hybrid deep learning CNN–ELM for age and gender classification
Duan, M., Li, K., Yang, C., & Li, K. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.08.062 · Cited by 258
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
Face Recognition Vendor Test (FRVT)
Grother, P., & Ngan, M. (2014) · doi:10.6028/nist.ir.8009 · Cited by 50
Metadata
- Authors
- Chang, K.-Y., Chen, C.-S., & Hung, Y.-P.
- Venue
- CVPR 2011
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 585-592
- OpenAlex
- W2009088607
- Cited by
- 342
- References
- 27
- FWCI
- 8.85
- Type
- conference paper
- Language
- en
- Open access
- closed
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