In the past 10 years, Soft-Biometrics recognition using 3D face has become prevailing, with many successful research works developed. In contrast, the usage of facial parts for Soft-Biometrics recognition remains less investigated. In particular, the nasal shape contains rich information for demographic perception. They are usually free from hair/glasses occlusions, and stay robust to facial expressions, which are challenging issues 3D face analysis. In this work, we propose the idea of 3D nasal Soft-Biometrics recognition. To this end, the simple 3D coordinates features are derived from the radial curves representation of the 3D nasal shape. With the 466 earliest scans of FRGCv2 dataset (mainly neutral), we achieved 91% gender (Male/Female) and 94% ethnicity (Asian/Non-asian) classification rates in 10-fold cross-validation. It demonstrates the richness of the nasal shape in presenting the two Soft-Biometrics, and the effectiveness of the proposed recognition scheme. The performances are further confirmed by more rigorous cross-dataset experiments, which also demonstrates the generalization ability of propose approach. When experimenting on the whole FRGCv2 dataset (40% are expressive), comparable recognition performances are achieved, which confirms the general knowledge that the nasal shape stays robust during facial expressions.


Nasal Soft-Biometrics 3D Gender/Ethnicity recognition 


  1. 1.
  2. 2.
    Phillips, P.J., et al.: Overview of the face recognition grand challenge. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1. IEEE (2005)Google Scholar
  3. 3.
    Drira, H., Amor, B.B., Srivastava, A., Daoudi, M., Slama, R.: 3D face recognition under expressions, occlusions, and pose variations. IEEE Trans. Pattern Anal. Mach. Intell. 35(9), 2270–83 (2013)CrossRefGoogle Scholar
  4. 4.
    Amor, B.B., Drira, H., Berretti, S., Daoudi, M., Srivastava, A.: 4-D facial expression recognition by learning geometric deformations. IEEE Trans. Cybern. 44(12), 2443–2457 (2014)CrossRefGoogle Scholar
  5. 5.
    Ballihi, L., Amor, B.B., Daoudi, M., Srivastava, A., Aboutajdine, D.: Boosting 3-D-geometric features for efficient face recognition and gender classification. IEEE Trans. Inf. Forensics Secur. 7(6), 1766–1779 (2012)CrossRefGoogle Scholar
  6. 6.
    Xia, B., Amor, B.B., Drira, H., Daoudi, M., Ballihi, L.: Combining face averageness and symmetry for 3D-based gender classification. Pattern Recogn. 48(3), 746–758 (2015)CrossRefGoogle Scholar
  7. 7.
    Ng, C.B., Tay, Y.H., Goi, B.M.: Vision-based human gender recognition: A survey. arXiv preprint arXiv:1204.1611 (2012)
  8. 8.
    Guo, G.: Human age estimation and sex classification. In: Shan, C., Porikli, F., Xiang, T., Gong, S. (eds.) Video Analytics for Business Intelligence. SCI, vol. 409, pp. 101–131. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-28598-1_4 CrossRefGoogle Scholar
  9. 9.
    Han, H., Otto, C., Liu, X., Jain, A.: Demographic estimation from face images: human vs machine performance. IEEE Trans. Pattern Anal. Mach. Intell. 37(6), 1148–1161 (2014).
  10. 10.
    Fu, S., He, H., Hou, Z.G.: Learning race from face: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 36(12), 2483–2509 (2014)CrossRefGoogle Scholar
  11. 11.
    Xia, B., Amor, B.B., Huang, D., Daoudi, M., Wang, Y., Drira, H.: Enhancing gender classification by combining 3D and 2D face modalities. In: 21st European Signal Processing Conference, pp. 1–5 (2013)Google Scholar
  12. 12.
    Zhang, W., Smith, M., Smith, L., Farooq, A.: Gender recognition from facial images: 2D or 3D? J. Optical Soc. Am. A 33(3), 333–344 (2016). ISSN 1084-7529CrossRefGoogle Scholar
  13. 13.
    Ezghari, S., Belghini, N., Zahi, A., Zarghili, A.: A gender classification approach based on 3D depth-radial curves and fuzzy similarity based classification. In: Intelligent Systems and Computer Vision (ISCV), pp. 1–6 (2015)Google Scholar
  14. 14.
    Toderici, G., O’malley, S.M., Passalis, G., Theoharis, T., Kakadiaris, I.A.: Ethnicity-and gender-based subject retrieval using 3-D face-recognition techniques. Int. J. Comput. Vision 89(2–3), 382–391 (2010)CrossRefGoogle Scholar
  15. 15.
    Wang, X., Kambhamettu, C.: Gender classification of depth images based on shape and texture analysis. In: Global Conference on Signal and Information Processing (GlobalSIP), pp. 1077–1080. IEEE (2013)Google Scholar
  16. 16.
    Gilani, S.Z., Shafait, F., Mian, A.: Biologically significant facial landmarks: how significant are they for gender classification? In: 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8 (2013)Google Scholar
  17. 17.
    Huang, D., Ding, H., Wang, C., Wang, Y., Zhang, G., Chen, L.: Local circular patterns for multi-modal facial gender and ethnicity classification. Image Vis. Comput. 32(12), 1181–1193 (2014)CrossRefGoogle Scholar
  18. 18.
    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? J. Percept. 22(2), 131–152 (1993)CrossRefGoogle Scholar
  19. 19.
    Jennifer, A., Jennifer, C., Jill, C., Philip, S., Andrew, M.: The Effect of Ethnicity on 2D and 3D Frontomaxillary Facial Angle Measurement in the First Trimester, Obstetrics and Gynecology International (2013)Google Scholar
  20. 20.
    Farkas, L.G., Katic, M.J., Forrest, C.R.: International anthropometric study of facial morphology in various ethnic groups/races. J. Craniofac. Surg. 16(4), 615–646 (2005)CrossRefGoogle Scholar
  21. 21.
    Andreu, Y., Mollineda, R.A.: The role of face parts in gender recognition. In: Campilho, A., Kamel, M. (eds.) ICIAR 2008. LNCS, vol. 5112, pp. 945–954. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-69812-8_94 CrossRefGoogle Scholar
  22. 22.
    Hu, Y., Yan, J., Shi, P.: A fusion-based method for 3D facial gender classification. In: 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), vol. 5. IEEE (2010)Google Scholar
  23. 23.
    Han, X., Ugail, H., Palmer, I.: Gender classification based on 3D face geometry features using SVM. In: CyberWorlds, pp. 114–118 (2009)Google Scholar
  24. 24.
    Bagdanov, A.D., Del Bimbo, A., Masi, I.: The florence 2D/3D hybrid face dataset. In: Proceedings of the 2011 Joint ACM Workshop on Human Gesture and Behavior Understanding, pp. 79–80 (2011). ISBN 978-1-4503-0998-1Google Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Infolab21, School of Computing and CommunicationsLancaster UniversityLancasterUK

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