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.
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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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Hawraa Abbas is a Ph.D. student at School of Engineering, Cardiff University; her scholarship is funded by the Iraqi government. She received her B.Sc. degree in computer engineering from Baghdad University, Iraq and M.Sc. degree in computer engineering also from Baghdad University, Iraq. Her research interests include 3D face modeling, classification of facial traits, image processing, computer network design, and genetic associations.
Yulia Hicks is a senior lecturer at School of Engineering, Cardiff University, specialising in statistical modeling, multi-modal signal processing, and computer vision. She leads the Sensors, Signals and Imaging Research Group and in the past several years she has led and been involved in a number of research council and industry funded research projects on video-assisted blind source separation, modeling and recognition of human motion and behavior, signal processing, and statistical modeling. She is also a co-director of the Human Factors Technology Lab, an interdisciplinary research lab at Cardiff University.
David Marshall has been working in the field of computer vision since 1986. In 1989, he joined Cardiff University as a lecturer and is now professor of computer vision in the School of Computer Science and Informatics. David’s research interests include articulated modelling of human faces, models of human motion, statistical modelling, high dimensional subspace analysis, audio/video image processing, and data/sensor fusion. He has published over 150 papers and one book in these research areas and has attracted over £4M in research funding over his academic career. He is currently Head of the Visual Computing Research Group and Director of the Human Factors Technology Centre. http://users.cs.cf.ac.uk/Dave.Marshall/.
Alexei I. Zhurov B.Sc., M.Sc. (Dolgoprudny, Russia), Ph.D. (Moscow, Russia), is a research officer at Cardiff University Dental School. Educated as an applied mathematician and physicist, he is working in the field of three-dimensional imaging of the human face and tissue biomechanics. His areas of research also include non-linear mechanics, exact solution methods for differential equations, fluid mechanics, mathematical statistics, and computer algebra. He has published over 100 research papers and three books.
Stephen Richmond B.D.S. (Sheffield), MSc.D. (Cardiff), Ph.D. (Manchester), D.Orth., R.C.S., F.D.S., R.C.S. (Edin), F.D.S., R.C.S. (Eng), FHEA, Head of Applied Clinical Research and Public Health in the Orthodontic Department, Cardiff University Dental School. His research has focused on facial variation (biological make-up, anatomy, facial surface morphology and function) to inform a fully functioning biomechanical head model.
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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. Comp. Visual Media 4, 17–32 (2018). https://doi.org/10.1007/s41095-017-0097-1
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DOI: https://doi.org/10.1007/s41095-017-0097-1