scholarly journals Skeleton based gait recognition for long and baggy clothes

2019 ◽  
Vol 277 ◽  
pp. 03005
Author(s):  
Abrar Alharbi ◽  
Fahad Alharbi ◽  
Eiji Kamioka

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%.


Author(s):  
Azhin T. Sabir

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.



2021 ◽  
Vol 10 (3) ◽  
pp. 202-208
Author(s):  
Azhin Tahir Sabir

Human gait identification is a behavioral biometric technology which can be used to monitor human beings without user interaction. Recent researches are more focused on investigating gait as one of the biometric traits.  Further, gait recognition aims to analyze and identify human behavioral activities and may be implemented in different scenarios including access control and criminal analysis. However, using various techniques in relation to image processing and obtaining better accuracy are remaining challenges. In last decade, Microsoft has introduced the Kinect sensor as an innovative sensor to provide image characteristics, precisely. Therefore, this article uses a Kinect sensor to extract gait characteristics to be used in individual recognition. A set of Triangulated shape are generated as new feature vector and called Triangulated Skeletal Model (TSM). Nearest Neighbor technique is utilized to do the recognition issue based on leave-one-out strategy. The experimental outcomes indicated that the recommended technique provides significant results and outperforms other comparative similar techniques with accuracy of 93.46%.  



2017 ◽  
Vol 26 (12) ◽  
pp. e382-e389 ◽  
Author(s):  
James D. Wilson ◽  
Jennifer Khan-Perez ◽  
Dominic Marley ◽  
Susan Buttress ◽  
Michael Walton ◽  
...  


2012 ◽  
Vol 19 (2) ◽  
pp. 4-10 ◽  
Author(s):  
Zhengyou Zhang


2020 ◽  
pp. 114179
Author(s):  
Lourdes Ramirez Cerna ◽  
Edwin Escobedo Cardenas ◽  
Dayse Garcia Miranda ◽  
David Menotti ◽  
Guillermo Camara-Chavez


Sensors ◽  
2017 ◽  
Vol 17 (2) ◽  
pp. 286 ◽  
Author(s):  
Ali Al-Naji ◽  
Kim Gibson ◽  
Sang-Heon Lee ◽  
Javaan Chahl


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