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On Combining Face Local Appearance and Geometrical Features for Race Classification

  • Fabiola Becerra-RieraEmail author
  • Nelson Méndez Llanes
  • Annette Morales-González
  • Heydi Méndez-Vázquez
  • Massimo Tistarelli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)

Abstract

In the field of demographic attribute classification, race estimation is perhaps the least studied topic in the literature. CNN-based approaches report the best results to the day, but they are computational expensive for practical applications. We propose a simpler approach by combining local appearance and geometrical features to describe face images, and to exploit the race information from different face parts by means of a component-based methodology. Experimental results obtained in the FERET subset from EGA database, with traditional but effective classifiers like Random Forest and Support Vector Machines, are very close to those achieved with a recent deep learning proposal.

Keywords

Soft-biometrics Race classification Face appearance representation Face anthropometric representation 

Notes

Acknowledgment

This research work has been partially supported by a grant from the European Commission (H2020 MSCA RISE 690907 “IDENTITY”) and by a grant of the Italian Ministry of Research (PRIN 2015).

References

  1. 1.
    Afifi, M., Abdelhamed, A.: Afif4: Deep gender classification based on adaboost-based fusion of isolated facial features and foggy faces. arXiv preprint arXiv:1706.04277 (2017)
  2. 2.
    Anwar, I., Islam, N.U.: Learned features are better for ethnicity classification. Cybern. Inf. Technol. 17(3), 152–164 (2017)Google Scholar
  3. 3.
    Becerra-Riera, F., Méndez-Vázquez, H., Morales-González, A., Tistarelli, M.: Age and gender classification using local appearance descriptors from facial components. In: International Joint Conference on Biometrics (IJCB), pp. 799–804 (2017)Google Scholar
  4. 4.
    Bekhouche, S.E., Ouafi, A., Dornaika, F., Taleb-Ahmed, A., Hadid, A.: Pyramid multi-level features for facial demographic estimation. Expert. Syst. Appl. 80(C), 297–310 (2017)CrossRefGoogle Scholar
  5. 5.
    Carcagnì, P., Coco, M.D., Cazzato, D., Leo, M., Distante, C.: A study on different experimental configurations for age, race, and gender estimation problems. EURASIP J. Image Video Process. 2015(1), 37 (2015)CrossRefGoogle Scholar
  6. 6.
    Cheng, J., Wang, P., Li, G., Hu, Q., Lu, H.: Recent advances in efficient computation of deep convolutional neural networks. Front. Inf. Technol. Electron. Eng. 19(1), 64–77 (2018)CrossRefGoogle Scholar
  7. 7.
    Fu, S., He, H., Hou, Z.G.: Learning race from face: a survey. Trans. Pattern Anal. Mach. Intell. (TPAMI) 36(12), 2483–2509 (2014)CrossRefGoogle Scholar
  8. 8.
    Gill, G., Hughes, S., Bennett, S., Miles Gilbert, B.: Racial identification from the midfacial skeleton with special reference to american indians and whites. J. Forensic Sci. 33(1), 92–99 (1988)CrossRefGoogle Scholar
  9. 9.
    González-Sosa, E., Fiérrez, J., Vera-Rodríguez, R., Alonso-Fernández, F.: Facial soft biometrics for recognition in the wild: recent works, annotation and cots evaluation. IEEE Trans. Inf. Forensics Secur. 13(7), 2001–2014 (2018)CrossRefGoogle Scholar
  10. 10.
    Gould, S., Fulton, R., Koller, D.: Decomposing a scene into geometric and semantically consistent regions. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8, September 2009Google Scholar
  11. 11.
    Kumar, N., Berg, A.C., Belhumeur, P.N., Nayar, S.K.: Describable visual attributes for face verification and image search. Trans. Pattern Anal. Mach. Intell. (TPAMI) 33(10), 1962–1977 (2011)CrossRefGoogle Scholar
  12. 12.
    Manesh, F.S., Ghahramani, M., Tan, Y.P.: Facial part displacement effect on template-based gender and ethnicity classification. In: 11th International Conference on Control Automation Robotics Vision, pp. 1644–1649. IEEE (2010)Google Scholar
  13. 13.
    Ou, Y., Wu, X., Qian, H., Xu, Y.: A real time race classification system. In: International Conference on Information Acquisition. IEEE (2005)Google Scholar
  14. 14.
    Riccio, D., Dugelay, J.L.: Geometric invariants for 2D/3D face recognition. Pattern Recognit. Lett. 28(14), 1907–1914 (2007)CrossRefGoogle Scholar
  15. 15.
    Riccio, D., Tortora, G., Marsico, M.D., Wechsler, H.: EGA - ethnicity, gender and age, a pre-annotated face database. In: Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS), pp. 1–8. IEEE (2012)Google Scholar
  16. 16.
    Roomi, S.M.M., Virasundarii, S.L., Selvamegala, S., Jeevanandhame, S., Hariharasudhan, D.: Race classification based on facial features. In: 3rd National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp. 54–57. IEEE (2011)Google Scholar
  17. 17.
    Salah, S.H., Du, H., Al-Jawad, N.: Fusing local binary patterns with wavelet features for ethnicity identification. Int. J. Comput. Inf. Syst. Control. Eng. 7, 330–336 (2013)Google Scholar
  18. 18.
    Shotton, J., Winn, J., Rother, C., Criminisi, A.: TextonBoost: joint appearance, shape and context modeling for multi-class object recognition and segmentation. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 1–15. Springer, Heidelberg (2006).  https://doi.org/10.1007/11744023_1CrossRefGoogle Scholar
  19. 19.
    Tamrakar, A., et al.: Evaluation of low-level features and their combinations for complex event detection in open source videos. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3681–3688 (2012)Google Scholar
  20. 20.
    Wang, W., He, F., Zhao, Q.: Facial ethnicity classification with deep convolutional neural networks. In: You, Z., et al. (eds.) CCBR 2016. LNCS, vol. 9967, pp. 176–185. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46654-5_20CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Fabiola Becerra-Riera
    • 1
    Email author
  • Nelson Méndez Llanes
    • 1
  • Annette Morales-González
    • 1
  • Heydi Méndez-Vázquez
    • 1
  • Massimo Tistarelli
    • 2
  1. 1.Advanced Technologies Application Center (CENATAV)HavanaCuba
  2. 2.Computer Vision Laboratory PolComingUniversity of SassariSassariItaly

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