Download PDF

Computational Visual Media

, Volume 4, Issue 1, pp 17–32 | Cite as

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

  • Hawraa Abbas
  • Yulia Hicks
  • David Marshall
  • Alexei I. Zhurov
  • Stephen Richmond
Open Access
Research Article
First Online:
Received:
Accepted:
  • 52 Downloads

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 
Download to read the full article text

Notes

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.

References

  1. [1]
    Abdi, H.; Valentin, D.; Edelman, B.; O’Toole, A. J. More about the difference between men and women: Evidence from linear neural networks and the principal-component approach. Perception Vol. 24, No. 5, 539–562, 1995.CrossRefGoogle Scholar
  2. [2]
    Biederman, I. Recognition-by-components: A theory of human image understanding. Psychological Review Vol. 94, No. 2, 115–147, 1987.CrossRefGoogle Scholar
  3. [3]
    Brigham, J. C.; Barkowitz, P. Do “They all look alike?” The effect of race, sex, experience, and attitudes on the ability to recognize faces. Journal of Applied Social Psychology Vol. 8, No. 4, 306–318, 1978.CrossRefGoogle Scholar
  4. [4]
    Bruce, V.; Burton, A. M.; Hanna, E.; Healey, P.; Mason, O.; Coombes, A.; Fright, R.; Linney, A. Sex discrimination: How do we tell the difference between male and female faces? Perception Vol. 22, No. 2, 131–152, 1993.CrossRefGoogle Scholar
  5. [5]
    Burton, A. M.; Bruce, V.; Dench, N. What’s the difference between men and women? Evidence from facial measurement. Perception Vol. 22, No. 2, 153–176, 1993.CrossRefGoogle Scholar
  6. [6]
    Malpass, R. S.; Kravitz, J. Recognition for faces of own and other race. Journal of Personality and Social Psychology Vol. 13, No. 4, 330–334, 1969.CrossRefGoogle Scholar
  7. [7]
    O’Toole, A. J.; Peterson, J.; Deffenbacher, K. A. Another race effect for categorizing faces by sex. Perception Vol. 25, No. 6, 669–676, 1996.CrossRefGoogle Scholar
  8. [8]
    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
  9. [9]
    Enlow, D. H.; Moyers, R. E. Handbook of Facial Growth. Philadelphia: WB Saunders Company, 1982.Google Scholar
  10. [10]
    Shepherd, J. The face and social attribution. In: Handbook of Research on Face Processing. Young, A. W.; Ellis, H. D. Eds. Elsevier, 289–320, 1989.CrossRefGoogle Scholar
  11. [11]
    Queirolo, C. C.; Silva, L.; Bellon, O. R.; Segundo, M. P. 3D face recognition using simulated annealing and the surface interpenetration measure. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 32, No. 2, 206–219, 2010.CrossRefGoogle Scholar
  12. [12]
    O’Toole, A. J.; Vetter, T.; Troje, N. F.; Bülthoff, H. H. Sex classification is better with three-dimensional head structure than with image intensity information. Perception Vol. 26, No. 1, 75–84, 1997.CrossRefGoogle Scholar
  13. [13]
    Bruce, V.; Young, A. Understanding face recognition. British Journal of Psychology Vol. 77, No. 3, 305–327, 1986.CrossRefGoogle Scholar
  14. [14]
    Farkas, L. G. Anthropometry of the Head and Face. Raven Pr, 1994.Google Scholar
  15. [15]
    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
  16. [16]
    Gupta, S.; Markey, M. K.; Bovik, A. C. Anthropometric 3D face recognition. International Journal of Computer Vision Vol. 90, No. 3, 331–349, 2010.CrossRefGoogle Scholar
  17. [17]
    Abbas, H.; Hicks, Y.; Marshall, D. Automatic classification of facial morphology for medical applications. Procedia Computer Science Vol. 60, 1649–1658, 2015.CrossRefGoogle Scholar
  18. [18]
    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
  19. [19]
    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
  20. [20]
    Lu, X.; Jain, A. K.; Colbry, D. Matching 2.5D face scans to 3D models. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 28, No. 1, 31–43, 2006.CrossRefGoogle Scholar
  21. [21]
    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
  22. [22]
    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
  23. [23]
    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
  24. [24]
    Ballihi, L.; Amor, B. B.; Daoudi, M.; Srivastava, A.; Aboutajdine, D. Boosting 3-D-geometric features for efficient face recognition and gender classification. IEEE Transactions on Information Forensics and Security Vol. 7, No. 6, 1766–1779, 2012.CrossRefGoogle Scholar
  25. [25]
    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
  26. [26]
    Zhang, L.; Chen, X.-Q.; Kong, X.-Y.; Huang, H. Geodesic video stabilization in transformation space. IEEE Transactions on Image Processing Vol. 26, No. 5, 2219–2229, 2017.MathSciNetCrossRefGoogle Scholar
  27. [27]
    Toma, A. M. Characterization of normal facial features and their association with genes. Ph.D. Thesis. Cardiff University, 2014.Google Scholar
  28. [28]
    Shi, J.; Samal, A.; Marx, D. How effective are landmarks and their geometry for face recognition? Computer Vision and Image Understanding Vol. 102, No. 2, 117–133, 2006.CrossRefGoogle Scholar
  29. [29]
    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
  30. [30]
    Brown, E.; Perrett, D. I. What gives a face its gender? Perception Vol. 22, No. 7, 829–840, 1993.CrossRefGoogle Scholar
  31. [31]
    Henderson, J.; Sherriff, A.; Farrow, A.; Ayres, J. G. Household chemicals, persistent wheezing and lung function: Effect modification by atopy? European Respiratory Journal Vol. 31, No. 3, 547–554, 2008.CrossRefGoogle Scholar
  32. [32]
    Avon longitudinal study of parents and children. 2017. Available at http://www.bristol.ac.uk/alspac/researchers data-access/data-dictionary.Google Scholar
  33. [33]
    Kau, C. H.; Richmond, S. Three-dimensional analysis of facial morphology surface changes in untreated children from 12 to 14 years of age. American Journal of Orthodontics and Dentofacial Orthopedics Vol. 134, No. 6, 751–760, 2008.CrossRefGoogle Scholar
  34. [34]
    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
  35. [35]
    Toma, A. M.; Zhurov, A.; Playle, R.; Ong, E.; Richmond, S. Reproducibility of facial soft tissue landmarks on 3D laser-scanned facial images. Orthodontics & Craniofacial Research Vol. 12, No. 1, 33–42, 2009.CrossRefGoogle Scholar
  36. [36]
    Taubin, G. Estimating the tensor of curvature of a surface from a polyhedral approximation. In: Proceedings of the 5th International Conference on Computer Vision, 902–907, 1995.CrossRefGoogle Scholar
  37. [37]
    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
  38. [38]
    Sun, X.; Morvan, J.-M. Curvature measures, normal cycles and asymptotic cones. Actes des rencontres du CIRM Vol. 3, No. 1, 3–10, 2013.CrossRefGoogle Scholar
  39. [39]
    Alliez, P.; Cohen-Steiner, D.; Devillers, O.; Lévy, B.; Desbrun, M. Anisotropic polygonal remeshing. ACM Transactions on Graphics Vol. 22, No. 3, 485–493, 2003.CrossRefGoogle Scholar
  40. [40]
    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
  41. [41]
    Besl, P. J.; Jain, R. C. Invariant surface characteristics for 3D object recognition in range images. Computer Vision, Graphics, and Image Processing Vol. 33, No. 1, 33–80, 1986.CrossRefMATHGoogle Scholar
  42. [42]
    Dorai, C.; Jain, A. K. COSMOS—A representation scheme for 3D free-form objects. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 19, No. 10, 1115–1130, 1997.CrossRefGoogle Scholar
  43. [43]
    Lappin, J. S.; Norman, J. F.; Phillips, F. Fechner, information, and shape perception. Attention, Perception, & Psychophysics Vol. 73, No. 8, 2353–2378, 2011.CrossRefGoogle Scholar
  44. [44]
    Mpiperis, I.; Malassiotis, S.; Strintzis, M. G. 3-D face recognition with the geodesic polar representation. IEEE Transactions on Information Forensics and Security Vol. 2, No. 3, 537–547, 2007.CrossRefGoogle Scholar
  45. [45]
    Surazhsky, V.; Surazhsky, T.; Kirsanov, D.; Gortler, S. J.; Hoppe, H. Fast exact and approximate geodesics on meshes. ACM Transactions on Graphics Vol. 24, No. 3, 553–560, 2005.CrossRefGoogle Scholar
  46. [46]
    Chen, J.; Han, Y. Shortest paths on a polyhedron. In: Proceedings of the 6th Annual Symposium on Computational Geometry, 360–369, 1990.Google Scholar
  47. [47]
    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
  48. [48]
    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
  49. [49]
    Liu, Y.-J. Exact geodesic metric in 2-manifold triangle meshes using edge-based data structures. Computer-Aided Design Vol. 45, No. 3, 695–704, 2013.CrossRefGoogle Scholar
  50. [50]
    Xu, C.; Wang, T. Y.; Liu, Y.-J.; Liu, L.; He, Y. Fast wavefront propagation (FWP) for computing exact geodesic distances on meshes. IEEE Transactions on Visualization and Computer Graphics Vol. 21, No. 7, 822–834, 2015.CrossRefGoogle Scholar
  51. [51]
    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
  52. [52]
    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
  53. [53]
    Sethian, J. A. Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science, Vol. 3. Cambridge University Press, 1999.MATHGoogle Scholar
  54. [54]
    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
  55. [55]
    Cohen, L. D.; Kimmel, R. Global minimum for active contour models: A minimal path approach. International Journal of Computer Vision Vol. 24, No. 1, 57–78, 1997.CrossRefGoogle Scholar
  56. [56]
    Peyré, G.; Cohen, L. Geodesic computations for fast and accurate surface remeshing and parameterization. Elliptic and Parabolic Problems 157–171, 2005.Google Scholar
  57. [57]
    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
  58. [58]
    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
  59. [59]
    Bekios-Calfa, J.; Buenaposada, J. M.; Baumela, L. Revisiting linear discriminant techniques in gender recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 33, No. 4, 858–864, 2011.CrossRefGoogle Scholar
  60. [60]
    Meyer, D.; Wien, F. T. Support vector machines. R News Vol. 1, No. 3, 23–26, 2001.MathSciNetGoogle Scholar
  61. [61]
    Wu, J.; Smith, W. A.; Hancock, E. R. Facial gender classification using shape-from-shading. Image and Vision Computing Vol. 28, No. 6, 1039–1048, 2010.CrossRefGoogle Scholar
  62. [62]
    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
  63. [63]
    Gower, J. C. Generalized procrustes analysis. Psychometrika Vol. 40, No. 1, 33–51, 1975.MathSciNetCrossRefMATHGoogle Scholar
  64. [64]
    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
  65. [65]
    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
  66. [66]
    Peyre, G. Fast marching MATLAB toolbox. Available at http://uk.mathworks.com/matlabcentral/fileexchange/6110-toolbox-fast-marching.Google Scholar
  67. [67]
    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
  68. [68]
    Bronstein, A. M.; Bronstein, M. M.; Kimmel, R. Three-dimensional face recognition. International Journal of Computer Vision Vol. 64, No. 1, 5–30, 2005.CrossRefGoogle Scholar
  69. [69]
    Hamza, A. B.; Krim, H. Geodesic matching of triangulated surfaces. IEEE Transactions on Image Processing Vol. 15, No. 8, 2249–2258, 2006.CrossRefGoogle Scholar
  70. [70]
    Raschka, S. About feature scaling and normalization. 2014. Available at https://sebastianraschka.com/Articles/2014 about feature scaling.html#aboutstandardization.Google Scholar
  71. [71]
    Fellous, J.-M. Gender discrimination and prediction on the basis of facial metric information. Vision Research Vol. 37, No. 14, 1961–1973, 1997.CrossRefGoogle Scholar
  72. [72]
    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
  73. [73]
    Palmer, S. E. Hierarchical structure in perceptual representation. Cognitive Psychology Vol. 9, No. 4, 441–474, 1977.CrossRefGoogle Scholar
  74. [74]
    Edelman, B.; Valentin, D.; Abdi, H. Sex classification of face areas: How well can a linear neural network predict human performance? Journal of Biological Systems Vol. 6, No. 3, 241–263, 1998.CrossRefGoogle Scholar
  75. [75]
    Abdelkader, M.; Leong, S.; White, P. S. Aesthetic proportions of the nasal aperture in 3 different racial groups of men. Archives of Facial Plastic Surgery Vol. 7, No. 2, 111–113, 2005.CrossRefGoogle Scholar
  76. [76]
    Akgüner, M.; Barutçu, A.; Karaca, C. Adolescent growth patterns of the bony and cartilaginous framework of the nose: A cephalometric study. Annals of Plastic Surgery Vol. 41, No. 1, 66–69, 1998.CrossRefGoogle Scholar
  77. [77]
    Prahl-Andersen, B.; Ligthelm-Bakker, A.; Wattel, E.; Nanda, R. Adolescent growth changes in soft tissue profile. American Journal of Orthodontics and Dentofacial Orthopedics Vol. 107, No. 5, 476–483, 1995.CrossRefGoogle Scholar
  78. [78]
    Lei, Y.; Lai, H.; Li, Q. Geometric features of 3D face and recognition of it by PCA. Journal of Multimedia Vol. 6, No. 2, 207–216, 2011.CrossRefGoogle Scholar
  79. [79]
    Ter Haar, F. B.; Veltkamp, R. C. A 3D face matching framework for facial curves. Graphical Models Vol. 71, No. 2, 77–91, 2009.CrossRefGoogle Scholar
  80. [80]
    Wilson, C.; Playle, R.; Toma, A.; Zhurov, A.; Ness, A.; Richmond, S. The prevalence of lip vermilion morphological traits in a 15-year-old population. American Journal of Medical Genetics Part A Vol. 161, No. 1, 4–12, 2013.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2017

Open Access The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www.editorialmanager.com/cvmj.

Authors and Affiliations

  • Hawraa Abbas
    • 1
    • 2
  • Yulia Hicks
    • 1
  • David Marshall
    • 3
  • Alexei I. Zhurov
    • 4
  • Stephen Richmond
    • 4
  1. 1.School of EngineeringKerbala UniversityKarbolaaIraq
  2. 2.School of EngineeringCardiff UniversityCardiffUK
  3. 3.School of Computer Science and InformaticsCardiff UniversityCardiffUK
  4. 4.School of DentistryCardiff UniversityCardiffUK

Personalised recommendations