Human Gait Recognition: Viewing Angle Effect on Normal Walking Pattern

2014 ◽  
Author(s):  
Nahid A. Makhdoomi ◽  
Teddy S. Gunawan ◽  
Mohamed H. Habaebi ◽  
Rosminazuin Ab. Rahim
Keyword(s):  
Gait Recognition ◽  
Human Gait ◽  
Viewing Angle ◽  
Normal Walking ◽  
Walking Pattern ◽  
Angle Effect

scholarly journals Gait Recognition as Non-Intrusive Biometric Using View Invariant Methods in Multi Temporal Images

2018 ◽  
Vol 7 (4.7) ◽  
pp. 127
Author(s):  
D. BEULAH DAVID ◽  
M. A.DORAIRANGASWAMY
Keyword(s):  
Face Detection ◽  
Gait Recognition ◽  
Viewing Angle ◽  
Gait Patterns ◽  
3D Images ◽  
Walking Pattern ◽  
Multi Temporal ◽  
Wrong Identification ◽  
Side Face ◽  
Time Image

Gait patterns have been used widely in recent years to authenticate users. Because it doesn’t require user intrusion, it is often used as a biometric to make authentication processes easier and hassle free. But there are various issues with this process. To start with, the viewing angle has to be constant which is quite difficult to achieve with limited number of cameras. Speed too can alter the way a person walks and cause inconsistencies in identification. Furthermore, complications might arise if the subject is carrying something. The weight can affect his walking pattern. Besides, a recent accident could also transform a person’s walking pattern and lead to wrong identification. Other biometrics such as face detection can be combined with this technique to reduce the issues leading to erroneous identification. In this paper, we propose a system to overcome the viewing angle discrepancies. The system takes in walking sequences as input and processes them to create images. This is converted into 3D images by means of stereovision algorithms. Using which, we can effectively match the real-time image with various image sequences in the database. Side face detection can enhance the accuracy further..  

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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