Advances in Vision-Based Gait Recognition: From Handcrafted to Deep Learning

Jashila Nair Mogan, Chin Poo Lee, Kian Ming Lim

Research output: Journal PublicationReview articlepeer-review

7 Citations (Scopus)

Abstract

Identifying people’s identity by using behavioral biometrics has attracted many researchers’ attention in the biometrics industry. Gait is a behavioral trait, whereby an individual is identified based on their walking style. Over the years, gait recognition has been performed by using handcrafted approaches. However, due to several covariates’ effects, the competence of the approach has been compromised. Deep learning is an emerging algorithm in the biometrics field, which has the capability to tackle the covariates and produce highly accurate results. In this paper, a comprehensive overview of the existing deep learning-based gait recognition approach is presented. In addition, a summary of the performance of the approach on different gait datasets is provided.

Original languageEnglish
Article number5682
JournalSensors
Volume22
Issue number15
DOIs
Publication statusPublished - Aug 2022
Externally publishedYes

Keywords

  • deep learning
  • gait recognition
  • review
  • vision-based

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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