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Computational Visual Media 2018, 4(1): 17-32 https://doi.org/10.1007/s41095-017-0097-1
Research Article | Open Access | Issue | Published: 22 January 2018

A 3D morphometric perspective for facial gender analysis and classification using geodesic path curvature features

Show Author's Information Hawraa Abbas1,2( ),  Yulia Hicks1,  David Marshall3,  Alexei I. Zhurov4,  Stephen Richmond4
School of Engineering, Kerbala University, Karbolaa, Iraq.
School of Engineering, Cardiff University, Cardiff, UK.
School of Computer Science and Informatics, Cardiff University, Cardiff, UK.
School of Dentistry, Cardiff University, Cardiff, UK.
Keywords:
ALSPAC dataset, gender classification, curvature features, geodesic curve
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Abbas H, Hicks Y, Marshall D, et al. A 3D morphometric perspective for facial gender analysis and classification using geodesic path curvature features. Computational Visual Media, 2018, 4(1): 17-32. https://doi.org/10.1007/s41095-017-0097-1
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Abstract Full text About this article

The relationship between the shape and gender of a face, with particular application to automatic gender classification, has been the subject of significant research in recent years. Determining the gender of a face, especially when dealing with unseen examples, presents a major challenge. This is especially true for certain age groups, such as teenagers, due to their rapid development at this phase of life. This study proposes a new set of facial morphological descriptors, based on 3D geodesic path curvatures, and uses them for gender analysis. Their goal is to discern key facial areas related to gender, specifically suited to the task of gender classification. These new curvature-based features are extracted along the geodesic path between two biological landmarks located in key facial areas.

Classification performance based on the new features is compared with that achieved using the Euclidean and geodesic distance measures traditionally used in gender analysis and classification. Five different experiments were conducted on a large teenage face database (4745 faces from the Avon Longitudinal Study of Parents and Children) to investigate and justify the use of the proposed curvature features. Our experiments show that the combination of the new features with geodesic distances provides a classification accuracy of 89%. They also show that nose-related traits provide the most discriminative facial feature for gender classification, with the most discriminative features lying along the 3D face profile curve.


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About this article

A 3D morphometric perspective for facial gender analysis and classification using geodesic path curvature features

Show Author's information Hawraa Abbas1,2( ), Yulia Hicks1, David Marshall3, Alexei I. Zhurov4, Stephen Richmond4
School of Engineering, Kerbala University, Karbolaa, Iraq.
School of Engineering, Cardiff University, Cardiff, UK.
School of Computer Science and Informatics, Cardiff University, Cardiff, UK.
School of Dentistry, Cardiff University, Cardiff, UK.

Abstract

The relationship between the shape and gender of a face, with particular application to automatic gender classification, has been the subject of significant research in recent years. Determining the gender of a face, especially when dealing with unseen examples, presents a major challenge. This is especially true for certain age groups, such as teenagers, due to their rapid development at this phase of life. This study proposes a new set of facial morphological descriptors, based on 3D geodesic path curvatures, and uses them for gender analysis. Their goal is to discern key facial areas related to gender, specifically suited to the task of gender classification. These new curvature-based features are extracted along the geodesic path between two biological landmarks located in key facial areas.

Classification performance based on the new features is compared with that achieved using the Euclidean and geodesic distance measures traditionally used in gender analysis and classification. Five different experiments were conducted on a large teenage face database (4745 faces from the Avon Longitudinal Study of Parents and Children) to investigate and justify the use of the proposed curvature features. Our experiments show that the combination of the new features with geodesic distances provides a classification accuracy of 89%. They also show that nose-related traits provide the most discriminative facial feature for gender classification, with the most discriminative features lying along the 3D face profile curve.

Keywords: ALSPAC dataset, gender classification, curvature features, geodesic curve

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Publication history
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Publication history

Revised: 08 August 2017
Accepted: 22 October 2017
Published: 22 January 2018
Issue date: March 2018

Copyright

© The Author(s) 2017

Acknowledgements

We are extremely grateful to all of the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses. The UK Medical Research Council and the Welcome Trust (Grant ref: 102215/2/13/2) and the University of Bristol provided core support for ALSPAC. This publication is the work of the authors and the first author Hawraa Abbas will serve as guarantor of the contents of this paper.

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