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)


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.


Gender recognition LBP Keypoint Descriptors 


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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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