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


Mogan, Jashila Nair and Lee, Chin Poo and Lim, Kian Ming (2022) Advances in Vision-Based Gait Recognition: From Handcrafted to Deep Learning. Sensors, 22 (15). p. 5682. ISSN 1424-8220

[img] Text
25.pdf - Published Version
Restricted to Repository staff only

Download (583kB)


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.

Item Type: Article
Uncontrolled Keywords: Gait recognition, vision-based, review, deep learning
Subjects: Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 20 Sep 2022 01:26
Last Modified: 20 Sep 2022 01:26


Downloads per month over past year

View ItemEdit (login required)