ML Anthology

Face Age Estimation Using Model Selection

Cuixian Chen, Yaw Chang, Karl Ricanek, Yishi Wang
CVPRW 2010 pp. 93-99
doi:10.1109/CVPRW.2010.5543820 /cvprw/2010/chen2010cvprw-face/

Abstract

Face age estimation is a difficult problem due to the dynamics of facial aging and its complex interactions owing to genetics and behavior factors. In this work we develop a robust age estimation system tuned by model selection that outperforms all prior systems on the FG-NET face database. We study various model selection methods systematically to determine the best selection methods among Least Angle Regression (LAR), Principle Component Analysis (PCA), and Locality Preserving Projections (LPP) for age estimation. Our performance analysis on PAL and FG-NET databases suggest that age estimation with LAR or LPP outperforms the full feature model. Furthermore, this work develops a novel operator named "graph age preserving" (GAP) to build a neighborhood graph for LPP for age estimation.

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Cite

Text

Chen et al. "Face Age Estimation Using Model Selection." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2010. doi:10.1109/CVPRW.2010.5543820

Markdown

[Chen et al. "Face Age Estimation Using Model Selection." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2010.](https://mlanthology.org/cvprw/2010/chen2010cvprw-face/) doi:10.1109/CVPRW.2010.5543820

BibTeX

@inproceedings{chen2010cvprw-face,
  title     = {{Face Age Estimation Using Model Selection}},
  author    = {Chen, Cuixian and Chang, Yaw and Ricanek, Karl and Wang, Yishi},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2010},
  pages     = {93-99},
  doi       = {10.1109/CVPRW.2010.5543820},
  url       = {https://mlanthology.org/cvprw/2010/chen2010cvprw-face/}
}