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On Combining Face Local Appearance and Geometrical Features for Race Classification

Part of the Lecture Notes in Computer Science book series (LNIP,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

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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).

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Correspondence to Fabiola Becerra-Riera .

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Becerra-Riera, F., Llanes, N.M., Morales-González, A., Méndez-Vázquez, H., Tistarelli, M. (2019). On Combining Face Local Appearance and Geometrical Features for Race Classification. In: Vera-Rodriguez, R., Fierrez, J., Morales, A. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2018. Lecture Notes in Computer Science(), vol 11401. Springer, Cham. https://doi.org/10.1007/978-3-030-13469-3_66

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  • DOI: https://doi.org/10.1007/978-3-030-13469-3_66

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