Gait Analysis for Gender Classification in Forensics

  • Paola BarraEmail author
  • Carmen Bisogni
  • Michele Nappi
  • David Freire-Obregón
  • Modesto Castrillón-Santana
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1123)


Gender Classification (GC) is a natural ability that belongs to the human beings. Recent improvements in computer vision provide the possibility to extract information for different classification/recognition purposes. Gender is a soft biometrics useful in video surveillance, especially in uncontrolled contexts such as low-light environments, with arbitrary poses, facial expressions, occlusions and motion blur. In this work we present a methodology for the construction of a gait analyzer. The methodology is divided into three major steps: (1) data extraction, where body keypoints are extracted from video sequences; (2) feature creation, where body features are constructed using body keypoints; and (3) classifier selection when such data are used to train four different classifiers in order to determine the one that best performs. The results are analyzed on the dataset Gotcha, characterized by user and camera either in motion.


Gender Classification Gait analysis Supervised learning SVC Random forest AdaBoost KNN 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Paola Barra
    • 1
    Email author
  • Carmen Bisogni
    • 1
  • Michele Nappi
    • 1
  • David Freire-Obregón
    • 2
  • Modesto Castrillón-Santana
    • 2
  1. 1.University of Salerno, Dipartimento di InformaticaFiscianoItaly
  2. 2.Universidad de Las Palmas de Gran Canaria (ULPGC)Las Palmas, Gran CanariaSpain

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