The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Human age estimation using bio-inspired features
Abstract
We investigate the biologically inspired features (BIF) for human age estimation from faces. As in previous bio-inspired models, a pyramid of Gabor filters are used at all positions of the input image for the S1units. But unlike previous models, we find that the pre-learned prototypes for the S2layer and then progressing to C2cannot work well for age estimation. We also propose to use Gabor filters with smaller sizes and suggest to determine the number of bands and orientations in a problem-specific manner, rather than using a predefined number. More importantly, we propose a new operator “STD” to encode the aging subtlety on faces. Evaluated on the large database YGA with 8,000 face images and the public available FG-NET database, our approach achieves significant improvements in age estimation accuracy over the state-of-the-artmethods. By applying our system to some Internet face images, we show the robustness of our method and the potential of cross-race age estimation, which has not been explored by any studies before.
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Cites
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680
Hierarchical models of object recognition in cortex
Riesenhuber, M., & Poggio, T. (1999) · Nature Neuroscience · doi:10.1038/14819 · Cited by 3,309
Cited by
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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
Self-citationOrdinal hyperplanes ranker with cost sensitivities for age estimation
Chang, K.-Y., Chen, C.-S., & Hung, Y.-P. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995437 · Cited by 342
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
A 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
Metadata
- Authors
- Guo, G., Mu, G., Fu, Y., & Huang, T. S.
- Venue
- 2009 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2009-06
- Pages
- 112-119
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2103077782
- Cited by
- 576
- References
- 2
- FWCI
- 17.99
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Guo, G., Mu, G., Fu, Y., & Huang, T. S. (2009). Human age estimation using bio-inspired features. 2009 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2009.5206681
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