Human Gait Recognition Based on Deterministic Learning and Data Stream of Microsoft Kinect

2019 ◽  
Vol 29 (12) ◽  
pp. 3636-3645 ◽  
Author(s):  
Muqing Deng ◽  
Cong Wang
Keyword(s):  
Data Stream ◽  
Gait Recognition ◽  
Microsoft Kinect ◽  
Human Gait ◽  
Deterministic Learning

scholarly journals Kinect-based human gait identifcation under different covariate factors

2018 ◽  
Vol 6 (2) ◽  
pp. 1-11
Author(s):  
Azhin T. Sabir
Keyword(s):  
Human Body ◽  
Gait Recognition ◽  
Video Quality ◽  
Microsoft Kinect ◽  
Human Gait ◽  
Human Beings ◽  
Linear Discriminant ◽  
Gait Biometrics ◽  
Gait Identification ◽  
Microsoft Kinect Sensor

Introduction: Nowadays human gait identification/recognition is available in a variety of applications due to rapid advances in biometrics technology. This makes them easier to use for security and surveillance. Due to the rise in terrorist attacks during the last ten years research has focused on the biometric traits in these applications and they are now capable of recognising human beings from a distance. The main reason for my research interest in Gait biometrics is because it is unobtrusive and requires lower image/video quality compared to other biometric traits. Materials and Methods: In this paper we propose investigating Kinect-based gait recognition using non-standard gait sequences. This study examines different scenarios to highlight the challenges of non-standard gait sequences. Gait signatures are extracted from the 20 joint points of the human body using a Microsoft Kinect sensor. Results and Discussion: This feature is constructed by calculating the distances between each two joint points from the 20 joint points of the human body provided which is known as the Euclidean Distance Feature (EDF). The experiments are based on five scenarios, and a Linear Discriminant Classifier (LDC) is used to test the performance of the proposed method. Conclusions: The results of the experiments indicate that the proposed method outperforms previous work in all scenarios.

scholarly journals Skeleton based gait recognition for long and baggy clothes

2019 ◽  
Vol 277 ◽  
pp. 03005
Author(s):  
Abrar Alharbi ◽  
Fahad Alharbi ◽  
Eiji Kamioka
Keyword(s):  
Gait Recognition ◽  
User Interaction ◽  
Microsoft Kinect ◽  
Human Gait ◽  
Kinect Sensor ◽  
Recognition Method ◽  
Walking Gait ◽  
Biometric Feature ◽  
Gait Database ◽  
Microsoft Kinect Sensor

Human gait is a significant biometric feature used for the identification of people by their style of walking. Gait offers recognition from a distance at low resolution while requiring no user interaction. On the other hand, other biometrics are likely to require a certain level of interaction. In this paper, a human gait recognition method is presented to identify people who are wearing long baggy clothes like Thobe and Abaya. Microsoft Kinect sensor is used as a tool to establish a skeleton based gait database. The skeleton joint positions are obtained and used to create five different datasets. Each dataset contained different combination of joints to explore their effectiveness. An evaluation experiment was carried out with 20 walking subjects, each having 25 walking sequences in total. The results achieved good recognition rates up to 97%.

Human gait recognition based on deterministic learning and knowledge fusion through multiple walking views

2020 ◽  
Vol 357 (4) ◽  
pp. 2471-2491 ◽  
Author(s):  
Muqing Deng ◽  
Tingchang Fan ◽  
Jiuwen Cao ◽  
Siu-Ying Fung ◽  
Jing Zhang
Keyword(s):  
Gait Recognition ◽  
Human Gait ◽  
Deterministic Learning ◽  
Knowledge Fusion

Model-Based Human Gait Recognition Via Deterministic Learning

2013 ◽  
Vol 6 (2) ◽  
pp. 218-229 ◽  
Author(s):  
Wei Zeng ◽  
Cong Wang ◽  
Yuanqing Li
Keyword(s):  
Gait Recognition ◽  
Human Gait ◽  
Deterministic Learning ◽  
Model Based

Human gait recognition via deterministic learning

2012 ◽  
Vol 35 ◽  
pp. 92-102 ◽  
Author(s):  
Wei Zeng ◽  
Cong Wang
Keyword(s):  
Gait Recognition ◽  
Human Gait ◽  
Deterministic Learning
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