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Volume 22
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10.3390/s22155682
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Review

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

by
Jashila Nair Mogan
,
Chin Poo Lee
* and
Kian Ming Lim
Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia
*
Author to whom correspondence should be addressed.
Sensors 2022, 22(15), 5682; https://doi.org/10.3390/s22155682
Submission received: 23 June 2022 / Revised: 4 July 2022 / Accepted: 11 July 2022 / Published: 29 July 2022
(This article belongs to the Section Sensor Networks)
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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.
Keywords: gait recognition; vision-based; review; deep learning gait recognition; vision-based; review; deep learning

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MDPI and ACS Style

Mogan, J.N.; Lee, C.P.; Lim, K.M. Advances in Vision-Based Gait Recognition: From Handcrafted to Deep Learning. Sensors 2022, 22, 5682. https://doi.org/10.3390/s22155682

AMA Style

Mogan JN, Lee CP, Lim KM. Advances in Vision-Based Gait Recognition: From Handcrafted to Deep Learning. Sensors. 2022; 22(15):5682. https://doi.org/10.3390/s22155682

Chicago/Turabian Style

Mogan, Jashila Nair, Chin Poo Lee, and Kian Ming Lim. 2022. "Advances in Vision-Based Gait Recognition: From Handcrafted to Deep Learning" Sensors 22, no. 15: 5682. https://doi.org/10.3390/s22155682

APA Style

Mogan, J. N., Lee, C. P., & Lim, K. M. (2022). Advances in Vision-Based Gait Recognition: From Handcrafted to Deep Learning. Sensors, 22(15), 5682. https://doi.org/10.3390/s22155682

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