A 3D morphometric perspective for facial gender analysis and classification using geodesic path curvature features
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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.
KeywordsALSPAC dataset gender classification curvature features geodesic curve
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.
- Golomb, B. A.; Lawrence, D. T.; Sejnowski, T. J. SEXNET: A neural network identifies sex from human faces. In: Proceedings of the Advances in Neural Information Processing Systems 3, Vol. 1, 2–8, 1990.Google Scholar
- Enlow, D. H.; Moyers, R. E. Handbook of Facial Growth. Philadelphia: WB Saunders Company, 1982.Google Scholar
- Farkas, L. G. Anthropometry of the Head and Face. Raven Pr, 1994.Google Scholar
- Gilani, S. Z.; Rooney, K.; Shafait, F.; Walters, M.; Mian, A. Geometric facial gender scoring: Objectivity of perception. PLoS ONE Vol. 9, No. 6, e99483, 2014.Google Scholar
- Lu, X.; Jain, A. K. Multimodal facial feature extraction for automatic 3D face recognition. Technical Report MSU-CSE-05-22. Michigan State University, 2005.Google Scholar
- Lu, X.; Jain, A. K. Automatic feature extraction for multiview 3D face recognition. In: Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition, 585–590, 2006.Google Scholar
- Perakis, P.; Theoharis, T.; Passalis, G.; Kakadiaris, I. A. Automatic 3D facial region retrieval from multi-pose facial datasets. In: Proceedings of the Eurographics Workshop on 3D Object Retrieval, 37–44, 2009.Google Scholar
- Xu, C.; Wang, Y.; Tan, T.; Quan, L. Automatic 3D face recognition combining global geometric features with local shape variation information. In: Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition, 308–313, 2004.Google Scholar
- Zhao, J.; Liu, C.; Wu, Z.; Duan, F.; Zhang, M.; Wang, K.; Jia, T. 3D facial similarity measure based on geodesic network and curvatures. Mathematical Problems in Engineering Vol. 2014, Article ID 832837, 2014.Google Scholar
- Li, X.; Jia, T.; Zhang, H. Expression-insensitive 3D face recognition using sparse representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2575–2582, 2009.Google Scholar
- Toma, A. M. Characterization of normal facial features and their association with genes. Ph.D. Thesis. Cardiff University, 2014.Google Scholar
- Han, X.; Ugail, H.; Palmer, I. Gender classification based on 3D face geometry features using SVM. In: Proceedings of the International Conference on CyberWorlds, 114–118, 2009.Google Scholar
- Avon longitudinal study of parents and children. 2017. Available at http://www.bristol.ac.uk/alspac/researchers data-access/data-dictionary.Google Scholar
- Fraser, A.; Macdonald-Wallis, C.; Tilling, K.; Boyd, A.; Golding, J.; Smith, G. D.; Henderson, J.; Macleod, J.; Molloy, L.; Ness, A.; Ring, S.; Nelson, S. M.; Lawlor, D. A. Cohort profile: The avon longitudinal study of parents and children: ALSPAC mothers cohort. International Journal of Epidemiology Vol. 42, No. 1, 97–110, 2013.CrossRefGoogle Scholar
- Cohen-Steiner, D.; Morvan, J.-M. Restricted delaunay triangulations and normal cycle. In: Proceedings of the 19th Annual Symposium on Computational Geometry, 312–321, 2003.Google Scholar
- Rusinkiewicz, S. Estimating curvatures and their derivatives on triangle meshes. In: Proceedings of the 2nd International Symposium on 3D Data Processing, Visualization and Transmission, 486–493, 2004.Google Scholar
- Chen, J.; Han, Y. Shortest paths on a polyhedron. In: Proceedings of the 6th Annual Symposium on Computational Geometry, 360–369, 1990.Google Scholar
- Xin, S.-Q.; Wang, G.-J. Improving Chen and Han’s algorithm on the discrete geodesic problem. ACM Transactions on Graphics Vol. 28, No. 4, Article No. 104, 2009.Google Scholar
- Crane, K.; Weischedel, C.; Wardetzky, M. Geodesics in heat: A new approach to computing distance based on heat flow. ACM Transactions on Graphics Vol. 32, No. 5, Article No. 152, 2013.Google Scholar
- Ying, X.; Wang, X.; He, Y. Saddle vertex graph (SVG): A novel solution to the discrete geodesic problem. ACM Transactions on Graphics Vol. 32, No. 6, Article No. 170, 2013.Google Scholar
- Ying, X.; Xin, S.-Q.; He, Y. Parallel Chen–Han (PCH) algorithm for discrete geodesics. ACM Transactions on Graphics Vol. 33, No. 1, Article No. 9, 2014.Google Scholar
- Zimmer, H.; Campen, M.; Kobbelt, L. Efficient computation of shortest path-concavity for 3D meshes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2155–2162, 2013.Google Scholar
- Peyré, G.; Cohen, L. Geodesic computations for fast and accurate surface remeshing and parameterization. Elliptic and Parabolic Problems 157–171, 2005.Google Scholar
- Aldridge, K.; George, I. D.; Cole, K. K.; Austin, J. R.; Takahashi, T. N.; Duan, Y.; Miles, J. H. Facial phenotypes in subgroups of prepubertal boys with autism spectrum disorders are correlated with clinical phenotypes. Molecular Autism Vol. 2, No. 1, 15, 2011.Google Scholar
- Gilani, S. Z.; Mian, A. Perceptual differences between men and women: A 3D facial morphometric perspective. In: Proceedings of the 22nd International Conference on Pattern Recognition, 2413–2418, 2014.Google Scholar
- Zhu, W.; Zeng, N.; Wang, N. Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations. In: Proceedings of NESUG: Health Care and Life Sciences, 1–9, 2010.Google Scholar
- Peyre, G. Toolbox graph—A toolbox to process graph and triangulated meshes. Available at http://uk.mathworks.com/matlabcentral/fileexchange/5355-toolbox-graph/content/toolbox graph/html/content. html.Google Scholar
- Drira, H.; Amor, B. B.; Daoudi, M.; Srivastava, A. Pose and expression-invariant 3D face recognition using elastic radial curves. In: Proceedings of the British Machine Vision Conference, 1–11, 2010.Google Scholar
- Peyre, G. Fast marching MATLAB toolbox. Available at http://uk.mathworks.com/matlabcentral/fileexchange/6110-toolbox-fast-marching.Google Scholar
- Gehrig, T.; Steiner, M.; Ekenel, H. K. Draft: Evaluation guidelines for gender classification and age estimation. Technical Report. Karlsruhe Institute of Technology, 2011.Google Scholar
- Raschka, S. About feature scaling and normalization. 2014. Available at https://sebastianraschka.com/Articles/2014 about feature scaling.html#aboutstandardization.Google Scholar
- Lu, X.; Chen, H.; Jain, A. K. Multimodal facial gender and ethnicity identification. In: Proceedings of the International Conference on Biometrics, 554–561, 2006.Google Scholar
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