Automatic Age Estimation from Face Images via Deep Ranking

Huei-Fang Yang, Bo-Yao Lin, Kuang-Yu Chang and Chu-Song Chen

Abstract

Automatic age estimation (AAE) from face images is a challenging problem because of large facial appearance variations resulting from a number of factors, e.g., aging and facial expressions. In this paper, we propose a generic, deep ranking model for AAE. Given a face image, our network first extracts features from the face through a scattering network (ScatNet), then reduces the feature dimension by principal component analysis (PCA), and finally predicts the age via category-wise rankers. The robustness of our approach comes from the following characteristics: (1) The scattering features are invariant to translation and small deformations; (2) the rank labels encoded in the network exploit the ordering relation among labels; and (3) the category-wise rankers perform age estimation within the same group. Our network achieves superior performance on a large-scale MORPH dataset and two expression ones, Lifespan and FACES.

Session

Poster 1

Files

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DOI

10.5244/C.29.55
https://dx.doi.org/10.5244/C.29.55

Citation

Huei-Fang Yang, Bo-Yao Lin, Kuang-Yu Chang and Chu-Song Chen. Automatic Age Estimation from Face Images via Deep Ranking. In Xianghua Xie, Mark W. Jones, and Gary K. L. Tam, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 55.1-55.11. BMVA Press, September 2015.

Bibtex

@inproceedings{BMVC2015_55,
	title={Automatic Age Estimation from Face Images via Deep Ranking},
	author={Huei-Fang Yang and Bo-Yao Lin and Kuang-Yu Chang and Chu-Song Chen},
	year={2015},
	month={September},
	pages={55.1-55.11},
	articleno={55},
	numpages={11},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.55},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.55}
}