Combination of Texture and Geometric Features for Age Estimation in Face Images

Marcos Vinicius Mussel Cirne, Helio Pedrini

Abstract

Automatic age estimation from facial images has recently received an increasing interest due to a variety of applications, such as surveillance, human-computer interaction, forensics, and recommendation systems. Despite such advances, age estimation remains an open problem due to several challenges associated with the aging process. In this work, we develop and analyze an automatic age estimation method from face images based on a combination of textural and geometric features. Experiments are conducted on the Adience dataset (Adience Benchmark, 2017; Eidinger et al., 2014), a large known benchmark used to evaluate both age and gender classification approaches.

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Paper Citation


in Harvard Style

Cirne M. and Pedrini H. (2018). Combination of Texture and Geometric Features for Age Estimation in Face Images.In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-290-5, pages 395-401. DOI: 10.5220/0006625503950401


in Bibtex Style

@conference{visapp18,
author={Marcos Vinicius Mussel Cirne and Helio Pedrini},
title={Combination of Texture and Geometric Features for Age Estimation in Face Images},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2018},
pages={395-401},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006625503950401},
isbn={978-989-758-290-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Combination of Texture and Geometric Features for Age Estimation in Face Images
SN - 978-989-758-290-5
AU - Cirne M.
AU - Pedrini H.
PY - 2018
SP - 395
EP - 401
DO - 10.5220/0006625503950401