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Gait-Based Gender Classification Using a Correlation-Based Feature Selection Technique

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(3), pp.55-66
  • DOI : 10.9708/jksci.2024.29.03.055
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : February 7, 2024
  • Accepted : March 6, 2024
  • Published : March 29, 2024

Beom Kwon 1

1동덕여자대학교

Accredited

ABSTRACT

Gender classification techniques have received a lot of attention from researchers because they can be used in various fields such as forensics, surveillance systems, and demographic studies. As previous studies have shown that there are distinctive features between male and female gait, various techniques have been proposed to classify gender from three dimensional(3-D) gait data. However, some of the gait features extracted from 3-D gait data using existing techniques are similar or redundant to each other or do not help in gender classification. In this study, we propose a method to select features that are useful for gender classification using a correlation-based feature selection technique. To demonstrate the effectiveness of the proposed feature selection technique, we compare the performance of gender classification models before and after applying the proposed feature selection technique using a 3-D gait dataset available on the Internet. Eight machine learning algorithms applicable to binary classification problems were utilized in the experiments. The experimental results show that the proposed feature selection technique can reduce the number of features by 22, from 82 to 60, while maintaining the gender classification performance.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.