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Authors: Kazuya Ueki 1 ; Masashi Sugiyama 2 and Yasuyuki Ihara 1

Affiliations: 1 NEC Soft, Ltd., Japan ; 2 Tokyo Institute of Technology and JST PRESTO, Japan

ISBN: 978-989-674-029-0

Keyword(s): Perceived age estimation, Active sample selection, Weighted regression, Semi-supervised learning, Manifold regularization, Human age perception.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing

Abstract: We address the problem of perceived age estimation from face images and propose a new semi-supervised age prediction method that involves two novel aspects. The first novelty is an efficient active learning strategy for reducing the cost of labeling face samples. Given a large number of unlabeled face samples, we reveal the cluster structure of the data and propose to label cluster representative samples for covering as many clusters as possible. This simple sampling strategy allows us to boost the performance of a manifold-based semisupervised learning method only with a relatively small number of labeled samples. The second contribution is to take the heterogeneous characteristics of human age perception into account. It is rare to misregard the age of a 5-year-old child as 15 years old, but the age of a 35-year-old person is often misregarded as 45 years old. Thus, magnitude of the error is different depending on subjects’ age. We carried out a largescale questionnaire survey for q uantifying human age perception characteristics and propose to encode the quantified characteristics by weighted regression. Consequently, our proposed method is expressed in the form of weighted least-squares with a manifold regularizer, which is scalable to massive datasets. Through real-world age estimation experiments, we demonstrate the usefulness of the proposed method. (More)

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Paper citation in several formats:
Ueki K.; Sugiyama M.; Ihara Y. and (2010). SEMI-SUPERVISED ESTIMATION OF PERCEIVED AGE FROM FACE IMAGES.In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-029-0, pages 319-324. DOI: 10.5220/0002817503190324

@conference{visapp10,
author={Kazuya Ueki and Masashi Sugiyama and Yasuyuki Ihara},
title={SEMI-SUPERVISED ESTIMATION OF PERCEIVED AGE FROM FACE IMAGES},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={319-324},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002817503190324},
isbn={978-989-674-029-0},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)
TI - SEMI-SUPERVISED ESTIMATION OF PERCEIVED AGE FROM FACE IMAGES
SN - 978-989-674-029-0
AU - Ueki, K.
AU - Sugiyama, M.
AU - Ihara, Y.
PY - 2010
SP - 319
EP - 324
DO - 10.5220/0002817503190324

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