Human Gait Recognition Using Skeleton Joint Coordinates with Orthogonal Least Square and Locally Linear Embedded Techniques

2019 ◽  
Author(s):  
Rohilah Sahak ◽  
Nooritawati Md Tahir ◽  
Ahmad Ihsan Mohd Yassin ◽  
Fadhlan Hafizhelmi Kamaruzaman ◽  
Ali Al Misreb
Keyword(s):  
Gait Recognition ◽  
Least Square ◽  
Human Gait ◽  
Joint Coordinates ◽  
Orthogonal Least Square ◽  
Locally Linear

scholarly journals Human gait recognition using orthogonal least square as feature selection

2020 ◽  
Vol 17 (3) ◽  
pp. 1355
Author(s):  
Rohilah Sahak ◽  
Nooritawati Md Tahir ◽  
Ahmad Ihsan Mohd Yassin ◽  
Fadhlan Hafizhelmi Kamaruzaman
Keyword(s):  
Recognition Accuracy ◽  
Gait Recognition ◽  
Least Square ◽  
Human Gait ◽  
Polynomial Kernel ◽  
Oblique View ◽  
3D Space ◽  
Gait Features ◽  
Rbf Kernel ◽  
Orthogonal Least Square

<span>This study investigates the potential gait features that are related to human recognition using orthogonal least square (OLS). Firstly, video of 30 subjects walking in oblique view was recorded using Kinect. Next, all 20 skeleton joints in 3D space were extracted and further selected using OLS. Additionally, SVM with linear, polynomial and radial basis function (RBF) kernel was used to classify the selected features. As consequences, OLS was proven to be able to identify the significant features using all three kernels of SVM since all recognition accuracy attained is higher as compared to the original gait features. Results attained showed that the highest recognition accuracy was 90.67% using 48 skeleton joint points for SVM with linear as kernel, followed by 46 skeleton joint points for SVM with RBF kernel namely 88.33% and accuracy of 86.33% for 38 skeleton joint points using  polynomial kernel.</span>

scholarly journals Kinect-Based Human Gait Recognition Using Locally Linear Embedded and Support Vector Machine

2018 ◽  
Vol 30 (2) ◽  
pp. 235-242
Author(s):  
Nooritawati Md Tahir ◽  
◽  
Rohilah Sahak ◽  
Ahmad Ihsan Mohd Yassin ◽  
Fadhlan Hafizhelmi Kamaruzaman
Keyword(s):  
Support Vector Machine ◽  
Gait Recognition ◽  
Human Gait ◽  
Support Vector ◽  
Locally Linear

Video-based Indoor Human Gait Recognition Using Depth Imaging and Hidden Markov Model: A Smart System for Smart Home

2010 ◽  
Vol 20 (1) ◽  
pp. 120-128 ◽  
Author(s):  
Md. Zia Uddin ◽  
Tae-Seong Kim ◽  
Jeong Tai Kim
Keyword(s):  
Hidden Markov ◽  
Gait Recognition ◽  
Smart Homes ◽  
The Elderly ◽  
Recognition System ◽  
Home Healthcare ◽  
Human Gait ◽  
Depth Imaging ◽  
Smart System ◽  
Spatiotemporal Features

Smart homes that are capable of home healthcare and e-Health services are receiving much attention due to their potential for better care of the elderly and disabled in an indoor environment. Recently the Center for Sustainable Healthy Buildings at Kyung Hee University has developed a novel indoor human activity recognition methodology based on depth imaging of a user’s activities. This system utilizes Independent Component Analysis to extract spatiotemporal features from a series of depth silhouettes of various activities. To recognise the activities from the spatiotemporal features, trained Hidden Markov Models of the activities would be used. In this study, this technique has been extended to recognise human gaits (including normal and abnormal). Since this system could be of great significance for the caring of the elderly, to promote and preserve their health and independence, the gait recognition system would be considered a primary function of the smart system for smart homes. The indoor gait recognition system is trained to detect abnormal gait patterns and generate warnings. The system works in real-time and is aimed to be installed at smart homes. This paper provides the information for further development of the system for their application in the future.

GSI: efficient spatio-temporal template for human gait recognition

2018 ◽  
Vol 10 (1) ◽  
pp. 29 ◽  
Author(s):  
Mohammad H. Ghaeminia ◽  
Shahriar B. Shokouhi
Keyword(s):  
Gait Recognition ◽  
Human Gait ◽  
Spatio Temporal

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 A Novel Human Gait Recognition System

2010 ◽  
pp. 1043-1047 ◽  
Author(s):  
Seyyed Meysam Hosseini ◽  
Abbas Nasrabadi ◽  
Peyman Nouri ◽  
Hasan Farsi
Keyword(s):  
Gait Recognition ◽  
Recognition System ◽  
Human Gait
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