Evaluation of Keypoint Descriptors for Gender Recognition

  • Florencia Soledad Iglesias
  • María Elena Buemi
  • Daniel Acevedo
  • Julio Jacobo-Berlles
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8827)

Abstract

Gender recognition is a relevant problem due to the number and importance of its possible application areas. The challenge is to achieve high recognition rates in the shortest possible time. Most studies are based on Local Binary Patterns (LBP) and its variants to estimate gender. In this paper, we propose the use of Binary Robust Independent Elementary Features (BRIEF), Oriented FAST and Rotated BRIEF (ORB) and Binary Robust Invariant Scalable Keypoints (BRISK) in gender recognition due to their good performance and speed. The aim is to show that ORB and BRISK are faster than LBP but allow to achieve similar recognition rates, which makes them suitable for real-time systems. For the best of our knowledge, it has not been studied in literature.

Keywords

Gender recognition LBP Keypoint Descriptors 

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References

  1. 1.
    Ahonen, T., Hadid, A., Piettikäinen, M.: Face description with local binary patterns: Application to face recognition. IEEE Transactions on pattern Analysis and Machine Intelligence 28(12) (2006)Google Scholar
  2. 2.
    Ahonen, T., Matas, J., Piettikäinen, M., He, C.: Rotation invariant image description with local binary pattern histogram fourier features. Scandinavian Conference on Image Analysis, SCIA (2009)Google Scholar
  3. 3.
    Andreu, Y., García-Sevilla, P., Mollineda, R.A.: Face gender classification: A statistical study when neutral and distorted faces are combined for training and testing purposes. Image and Vision Computing 32(1), 27 (2014)CrossRefGoogle Scholar
  4. 4.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    BekiosCalfa, J., Buenaposada, J.M., Baumela, L.: Revisiting linear discriminant techniques in gender recognition. IEEE Transactions on pattern Analysis and Machine Intelligence 33(4), 858–864 (2011)CrossRefGoogle Scholar
  6. 6.
    Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: Binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Leutenegger, S., Chli, M., Siegwart, R.Y.: Brisk: Binary robust invariant scalable keypoints. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2548–2555. IEEE (2011)Google Scholar
  8. 8.
    Li, M., Bao, S., Dong, W., Wang, Y., Su, Z.: Head-shoulder based gender recognition. In: IEEE International Conference on Image Processing (ICIP), pp. 2753–2756 (2013)Google Scholar
  9. 9.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision 1999, vol. 2, pp. 1150–1157 (1999)Google Scholar
  10. 10.
    Lu, L., Shi, P.: A novel fusion-based method for expression-invariant gender classification. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2009, pp. 1065–1068 (2009)Google Scholar
  11. 11.
    Muja, M., Lowe, D.: Fast matching of binary features. In: 2012 Ninth Conference on Computer and Robot Vision (CRV), pp. 404–410 (2012)Google Scholar
  12. 12.
    Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.J.: The feret database and evaluation procedure for face recognition algorithms. Image and Vision Computing 16(5), 295–306 (1998)CrossRefGoogle Scholar
  13. 13.
    Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns. In: Computational Imaging and Vision, vol. 40, Springer (2011)Google Scholar
  14. 14.
    Ramón-Balmaseda, E., Lorenzo-Navarro, J., Castrillón-Santana, M.: Gender classification in large databases. In: Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds.) CIARP 2012. LNCS, vol. 7441, pp. 74–81. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  15. 15.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: an eficient alternative to sift or surf. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571. IEEE (2011)Google Scholar
  16. 16.
    Thomaz, C.E., Giraldi, G.A.: A new ranking method for principal components analysis and its application to face image analysis. Image and Vision Computing 28(6), 902–913 (2010)CrossRefGoogle Scholar
  17. 17.
    Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar
  18. 18.
    Wang, J.G., Li, J., Yau, W.Y., Sung, E.: Boosting dense sift descriptors and shape contexts of face images for gender recognition. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 96–102 (2010)Google Scholar
  19. 19.
    Wang, J.G., Wang, H.L., Ye, M., Yau, W.Y.: Real-time gender recognition with unaligned face images. In: 2010 The 5th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 376–380 (June 2010)Google Scholar
  20. 20.
    Ylioinas, J., Hadid, A., Pietikäinen, M.: Combining contrast information and local binary patterns for gender classification. In: Image Analysis, pp. 676–686 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Florencia Soledad Iglesias
    • 1
  • María Elena Buemi
    • 1
  • Daniel Acevedo
    • 1
  • Julio Jacobo-Berlles
    • 1
  1. 1.Facultad de Ciencias Exactas y Naturales, Departamento de ComputaciónUniversidad de Buenos AiresArgentina

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